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Full text of "Air weapon systems in the Third World : a combat potential assessment technique"

NPS-56-86-001 



LIBRARY ' 

RESEARCH REPORTS" DIVISION 
NAVAL POSTGRADUATE SCHOOL 
MONTEREY, CALIFORNIA 93940 



NAVAL POSTGRADUATE SCHOOL 

Monterey, California 




AIR WEAPON SYSTEMS IN THE THIRD WORLD: 
A COMBAT POTENTIAL ASSESSMENT TECHNIQUE 



Christopher L. Christon 
Lieutenant Colonel, USAF 

June 1986 

Naval Postgraduate School 
Monterey, California 



Approved for public release; distribution unlimiited 



Prepared for: 



FedDocs 

D 208.14/2 

NPS-56-86-001 



HQ USAF/Assistant Chief of Staff Intelligence 

Director of Estimates 

Room 4A882 

Pentagon 

Washington, DC 20330 



NAVAL POSTGRADUATE SCHOOL 
Monterey, California 



Rear Admiral R. H. Shumaker David A. Schrady 

Superintendent Provost 

The work, reported herein was supported by HQ USAF/ Assistant Chief of 
Staff Intelligence, Director of Estimates, Room 4A882, Pentagon, Washington, 
D.C. The reproduction of all or part of this report is authorized. 



UNCLASSIFIED 



SECURITY CLASSIFICATION OF THIS RAGE (Whmn Data SnffOi 



REPORT DOCUMENTATION PAGE 



I REPORT NUMBER 

NPS-56-86-001 



READ INSTRUCTIONS 
BEFORE COMPLETING FORM 



2. GOVT ACCESSION NO 



I. RECIPIENT'S CATALOG NUMBER 



4. Tl TL E fand Submit, 

Air Weapon Systems in the Third World: A Combat 
Potential Assessment Technique 



5. TYRE OF REPORT a RERIOO COVERED 

Final Report 



« PERFORMING ORG. REPORT NUMBER 



7. AUTMORC*; 



t. CONTRACT OR GRANT NUMBERS 



Christopher L. Christon 
Lieutenant Colonel, USAF 



I. PERFORMING ORGANIZATION NAME AND ADDRESS 

Naval Postgraduate School 
Monterey, California 93943 



10. PROGRAM ELEMENT. PROJECT. TASK 
AREA * WORK UNIT NUMBERS 



II. CONTROLLING OFFICE NAME ANO AOORESS 

Intelligence Community Staff 
1724 F Street N.W. 
Washington. DC ?0006 



12. REPORT OATE 

June 1986 



IS. NUMBER OF PAGES 

206 



14 MONITORING AGENCY NAME * AOORESV" dltloront Inm Controlling Olllco) 

HQ USAF/ Assistant Chief of Staff Intelligence 
Director of Estimates, Room 4A882 
Pentagon, Washington, DC 20330 



IS. SECURITY CLASS, (ol thlm riporl) 

UNCLASSIFIED 



TIT DECLASSIFICATION/ DOWNGRADING 



SCHEDULE 



K. DISTRIBUTION STATEMENT (ol Ihla Report) 

Approved for public release; distribution unlimitec 



17. DISTRIBUTION STATEMENT (of tho abolracl mitrtit In Bloc* 20, It dlllormnl tram R*pon) 



It. SUPPLEMENTARY NOTES 



tS KEY WOROS f Continue on ravorao aide II nocooomrr and Identity by block numbor) 

Air weapon systems factor analysis 

multi-attribute utility technique 



Middle East 



arms transfers 

fighter aircraft 

combat capabilities assessment 



linear aggregation 



20. ABSTRACT (Contlnuo an rovoroo »ido II noeoomoty ond Idonlltr »r block mambor) 

This report, prepared under the auspices of the Director of Central 
Intelligence's Exceptional Intelligence Analyst Program, proposes a quantita- 
tive technique for assessing the impact of air weapons systems' transfers on 
the combat potential of Middle Eastern recipients. Existing quantitative 
methodologies are examined to identify strengths and weaknesses. An analytical 
paradigm is outlined, and essential measurement variables isolated. Data 
collection and estimation techniques are explained. A data reduction scheme 
based on an amalgam of factor analysis and mu 1 t i -a r r r i hnri> uril irv t-.-rhniqn,... 



DO , :°: M 7 , 1473 
(Page 1) 



EDITION OF I NOV «S IS OBSOLETE 
S/N 0102-014- 660 1 

1 



UNCLASSIFIED 



SECURITY CLASSIFICATION OF THIS PAOE (Whmn Dolm Mnloro*) 



UNCLASSIFIED 



f ii c u •" T • ei.iii" | C" | fiN a* ■'-is »«otfw*». i-)..« »»«.,»< 



is described and tested. A linear aggregational technique which combines the 
relative contributions of air weapons attributes and subsystems in four mission 
roles (air defense, fighter/air superiority, interdiction, close air support) 
is developed and applied to 125 operational and programmed aircraft. Current 
and projected combat aircraft inventories for 22 Middle Eastern/North African 
nations are established, and force propagation potential ("sortie production) is 
estimated for each aircraft. Sortie production and air weapon system combat 
potential assessments are combined to generate national force air combat poten- 
tial estimates in four mission areas for the period L984 through 1990. An 
example illustrating the proposed methodology's flexibility in responding to 
differing alternatives and assumptions in support of arms transfer policy 
making is offered. Detailed listings of raw study data, aircraft combat poten- 
tial estimates, inventories, and national force air combat potential are 
included. The study concludes that the proposed methodology is more comprehen- 
sive and sensitive to user demands than existing systems and warrants further 
evolution. 



DD i F r2Z™n 14T3 UNCLASSIFIED 

1 Jan i 3 



~/N 0102-014-G601 iccu^ity cla«h»icatiow o» Tw.t p*ccr»»»- oi 



AIR WEAPON SYSTEMS IN THE THIRD WORLD 
A COMBAT POTENTIAL ASSESSMENT TECHNIQUE 



Christopher L. Christon, Lieutenant Colonel, USAF 

Department of National Security Affairs 

Naval Postgraduate School 

Monterey, California 



June 1986 



EXECUTIVE SUMMARY 

Security assistance to the Third World will remain a vibrant topic in the American political dialogue for 
the foreseeable future. While specific issues are fraught with political, economic, ethical, and emotional 
overtones, analysis of the military dimension is inseparable from the decision making process. The mili- 
tary analyst's charter is to provide decision makers with comprehensive assessments of arms transfer alter- 
natives, probing their contributions to recipient force structure modernization and forecasting their 
impacts on regional military stability. 

In this pursuit, some form of quantitative analysis is inescapable, be it as simple as the tabulation of 
military inventories or as complex as a sophisticated war gaming model. No matter the complexity of the 
technique employed, its processes must be transparent to the decision maker and its content malleable to 
his priorites and perceptions. At the same time, the technique must be slaved to the objectives and com- 
ponents of the analytical question, not vice versa. To assist arms transfer policy making, the assessment 
of potential capabilities to conduct definable operations in a specific environment is vital. To do less is to 
leave critical stones unturned. 

Simple tabular techniques have a place in the panoply of military analysis, but their results can rarely 
be translated into militarily relevant conclusions. The systematized aggregation of performance and force 
propagation characteristics is an elemental attribute of any model which purports to assess combat capa- 
bilities. The objective of this research effort has been to develop a methodology wliich captures these 
facets and aggregates them according to their relative utilities in generating potential combat outputs. 

Using air weapon systems (125 aircraft) and the Middle East/North African region (22 countries) as a 
developmental test bed, the study began by evaluating the assets and liabilities of earlier aggregational 
methodologies. Factor analysis stood out because of its ability to consolidate multiple variables into 
common attribute performance measures. However, its combinational logic is haphazard when applied at 
the weapon system level, and its output measures are not legitimate candidates for aggregation at the force 
level. Multi-attribute utility technique produces a judgment based combinational matrix but is adminis- 
tratively unweildly and naturally applicable only to ratio level data. The weighted linear aggregation tech- 
nique developed by The Analytic Sciences Corporation incorporates expert judgment and processes data 
of any measurement level but cannot accommodate multi-variable attributes and is insensitive to per- 
formance variations within broadly defined subsystem categories. Whatever its strengths or weaknesses, 
each methodology demonstrated the criticality of solid and comprehensive data input to the production of 
meaningful results. 

- ii - 



To guide the data collection process, a matrix was developed the key elements of which constitute 
the components implicated in assessing force air combat capability. Two essential elements, air weapon 
system performance and force propagation potential, were positioned at the apex of the framework. They 
were divided into the subcomponents which define their basic dimensions. Along with the various cat- 
egories of subsystem, the air weapon system performance group included a family of factors which relate 
the subsystems in terms of configuration and combat utility. On the force propagation side of the ledger, 

inventory, mission allocation, and sortie generation subcomponents were identified. The importance of 

3 ... 

intangible factors such as operator proficiency and C I support was acknowledged, but their consideration 

deferred to other research efforts. Each subcomponent thus identified was further divided into the per- 
formance attributes which contribute to its operation. These were in turn subdivided into the variables 
which describe those attributes. 

Data collection was accomplished using open source data. Certain artifical constraints were estab- 
lished to expedite the process. Only fixed wing aircraft with direct combat application in recent or future 
Middle Eastern combat scenarios were considered. When data were unavailable, they were estimated 
using the most accurate technique which could be supported. In some instances, specific data values are 
consequently open to challenge. While the possible inaccuracies are lamentable, they are not fatal to the 
evaluation technique itself and can easily be revised in subsequent applications. Since the methodology 
aimed to support the development of future arms transfer policies, national air combat inventories were 
anchored with known data from the past two years and projected out to 1990. A unique data set was 
collected to determine the relative utilities of attributes and subsystems in definable combat roles. A panel 
of 25 fighter experts familiar with Middle Eastern air operations was polled to ascertain their views on the 
relationships which obtain among attributes and subsystems in four different mission areas. The results 
were synthesized statistically and recast as relational variable values to be employed during the weapon 
system combinational phase. 

Only after an analytical structure had been articulated and supporting data collected was a data 
reduction scheme devised, reversing the process followed in some other research efforts. Factor analysis 
was employed to create relative index values for attributes described by multiple variables. Targeted at the 
attribute level, this rniiiimalist version of the factor analysis methodology purged the indices of extraneous 
variable influences. Ratio properties were restored to the indices through the utilization of a zero-valued 
control case the factor score for which constituted a threshold from which other scores in the data set 
could be scaled. Variables described by nominal values were not included in the factor problems to pre- 
clude their distorting influences but were reserved for introduction in the aggregation process. 



tu 



The computational phase itself was adapted with a few major variations from the linear equations 
developed by The Analytic Sciences Corporation. The process was initiated at the bottom of the analyt- 
ical ladder, combining subsystem attributes. Expert assigned values for nominally described variables were 
used to modify the raw attribute scores extracted from the data reduction phase. Attribute scores were 
combined in accordance with their relative air combat utilities in each mission area. An analogous proce- 
dure was followed at the subcomponent and component levels, with the computations not only consider- 
ing relative utility values but also conforming to specific air weapon system configurations. The product 
is a set of relative combat potential scores (Air Combat Potential Units) for each of the 125 air weapon 
systems in whatever mission roles were appropriate. 

Force propagation values were computed in a somewhat different fashion. National aircraft invento- 
ries, mission allocations, operational availability rates, maintenance requirements, and maintenance 
resources were considered in a series of equations which computed the sortie generation potential for each 
possessed air weapon system in those roles to which it would likely be committed. To illustrate the 
impact of personnel force quality on sortie generation, an additional force level factor, the relative support 
index, was also injected into select force propagation equations. Since the variables on which the support 
index was predicated are considered 'soft' surrogates for personnel quality, its general application is not 
recommended. However, its profound influence testifies to the requirement for such intangibles to be 
considered objectively or subjectively in force propagation and air combat analysis. In the ultimate com- 
putational step, air weapon system mission potential and national force propagation potential were mated 
to produce an estimate of a country's air combat potential in four mission roles on a single day of flying. 

The results of the aggregation phase were reviewed to determine their efficacy both at the air weapon 
system and national force levels. The results conformed to intuitive assessments and poignantly demon- 
strated the desirability of employing a analytical scheme which aggregated the cumulative effects of system 
and force subcomponents on specific mission outputs. To further exercise the model, a phased analysis of 
a specific arms transfer proposal (advanced air defense fighters for Jordan) was conducted. The model 
showed itself to be responsive to the type of modifications a decision maker might stipulate in evaluating 
specific weapon system alternatives, gauging their contribution to force capabilities under varying condi- 
tions, and analyzing their impact on regional military balances under differing conflict scenarios. 

The air combat potential aggregation methodology proposed in this study is a powerful and flexible 
mechanism with which to analyze the composition, benefits, and liabilities of air weapon systems individ- 
ually and at the force and regional levels. Its underlying philosphy, analytical framework, and combina- 
tional scheme are extendable to other regions, categories of weapons, and analytical problems. But the 
present model has its drawbacks. Solely relying on unclassified data sources, values for some critical vari- 



- IV - 



ables had to be estimated. Consequent inaccuracies were inevitable. The linear combinational form used 
to aggregate values at each step in the process fails to capture the synergy among subcomponents, partic- 
ularly in force level calculations. Unquestionably vital factors such as operator proficiency, CI support, 
and the ground air defense environment were not considered in the prototype. These elements need to be 
introduced into a fully proficient model or considered in modifying its results. Finally, the prototype as 
currently configured is not amenable to 'user-friendly' micro-computer processing. Creation of a respon- 
sive micro-based system is eminently feasible but requires additional developmental effort. 

Each of these liabilities is surmountable and represents fertile ground for additional effort within the 
intelligence community. Utilizing the methodoligical framework and procedures, a classified data base 
could be easily created and expanded to include additional aircraft, subsytems, and regions. Analytical 
subsets addressing elements of the ground air defense environment could also be introduced into the 
model relatively painlessly. Of greater complexity is the development of algorithms which capture the 
synergy among system and force components. One possibility is to attempt adaptation of existing air 
combat simulations to define an alternative non-linear aggregational scheme. Integration of combat rele- 
vant intangibles is a similarly complex challenge. Reliable mathmatical representations might not prove 
possible, but the influences of operator proficiency and the like can be reasonably assessed by weapon 
system and regional experts and applied subjectively in interpreting model output. 

The air weapon system potential model is not a predictor of combat outcomes, but it does provide 
the decision maker with finely textured and responsive static indicators of individual weapon system and 
force potential. These indicators are essential points of departure in evaluating the military dimension of 
security assistance options. With the enhancements described above, the methodology developed in this 
research effort represents a productive vehicle for intelligence community participation in the security 
assistance policy development process. 



v - 



PREFACE 

This technical note was prepared under the auspices of the Director of Central Intelligence's Exceptional 
Intelligence Analyst Program. It was originally conceived as a wide-gauged historical treatment of arms 
transfers to the Persian Gulf/Southwest Asian region, the findings of which could serve as a base for 
future forecasting. From the outset, it was recognized that the essential cog in the analytical wheel was 
the methodology which portrayed the effects of military equipment transfers on recipient combat capabili- 
ties and regional stability. It had been assumed that existing analytical methodologies would be sufficient 
to the task. 

That assumption proved fallacious and caused a reorientation in study objectives. Development of a 
model to index and aggregate combat potential became the focal point of the research effort. Owing to a 
variety of factors, not the least of which was my own limited expertise, the field of study was further nar- 
rowed to air weapon systems. The temporal emphasis also changed as the study evolved. The develop- 
ment of a responsive mechanism to support future decision making emerged as a more compelling chal- 
lenge than charting the historical evolution of Middle Eastern air combat capabilities. 

The resultant methodological scheme, detailed in this technical report, does not meet all of the goals 
originally set out for it. Most significantly, the political dimension of United States' arms transfer policy 
toward the Middle East is not addressed; nor are the economic and security advantages and liabilities 
inherent in the process considered. These omissions notwithstanding, the proposed methodology delves 
much more deeply into the intricacies of air combat potential assessment than had been originally con- 
templated and than is available in current assessment systems. I trust this benefit will compensate for the 
aforementioned analytical lapses. 

Readers will note the methodology is cast as a policy assistance model, and most of the discussions 
revolve around its viability in that role. While some might consequently question its pertinence as an 
intelligence tool, my long-standing conviction is that policy development and intelligence analysis are 
inextricably meshed. In that light, the proposed methodology constitutes one among many tools which 
intelligence analysts can employ in assisting arms transfer decision makers. As an an air intelligence ana- 
lyst myself, I also believe the methodological structure, if not its content, can be profitably applied by 
colleagues assessing a variety of air threats and developments. 

I would like to express my warmest thanks to the Intelligence Community Stall for funding the 
project, to the Assistant Chief of Staff/ Intelligence, HQ USAF, for allowing me the opportunity to pursue 



vi - 



it, and to the Naval Postgraduate School for providing a most hospitable research venue. Special personal 
thanks are due Dr. Edward Laurance of the Department of National Security Affairs who initially inspired 
the project and channelled its course; to Colonel Jack L. Houlgate, HQ USAF, Directorate of Estimates, 
who served as a most understanding and efficient project manager; to Lieutenant Colonel Richard Forney 
of the Department of National Security Affairs who provided consistent technical and moral support; to 
Colonel John Garrison whose counsel on arms transfer issues and practices was invaluable; and to Colo- 
nel Michael (Nort) Nelson who served as my mentor in sorting through and consolidating air weapon 
system performance attributes. Several non-government entities also helped me over rough spots in the 
research and were particularly gracious in sharing perceptions and methodological concepts. These 
include Mr. J. E. Gibson and his staff at the Northrop Corporation, Mr. William Vogt of The Analytic 
Sciences Corporation, and Dr. Ronald Sherwin and Ms. Joyce Mullen of Third Point Systems Corpora- 
tion. 

Despite the profound impact these individuals and many like them have had on the conceptualiza- 
tion and preparation of this report, I have undoubtedly included some misperceptions or technical errors 
in the final version. These are my responsibility alone. 

The views expressed in this report are those of the author and do not represent the official position 
of the Naval Postgraduate School, the United States Air Force, the Department of Defense, the Intelli- 
gence Community Staff, or the United States Government. 



vu - 



CONTENTS 

Executive Summary ii 

Preface vi 

Chapter 1: Arms to the Third World 1 

Introduction 1 

The Dynamics of International Arms Trade 1 

The American Dilemma 2 

To Trade or Not To Trade 4 

Military Analysis and Arms Transfer Policy 4 

The Role of Military Analysis 4 

Principles of Military Analysis 5 

Research Methodology 6 

Objectives 6 

Limitations 6 

Organization 7 

Chapter 2: Methodologies Review 9 

General 9 

Counting Dollars' 9 

Counting 'Beans' 10 

Factor Analysis 12 

Description 12 

Factor Analyzing Air Weapons Systems 13 

Defining Factors 13 

Extracting Factor Scores 1 8 

Using Factor Scores 20 

Factor Analysis Summary 21 

Multi-Attribute Utility Theory 21 

Description 22 

Application 22 

Multi-Attribute Utility Technique Summary 27 

TASCFORM Force Modernization Model 27 

Description 28 

Application 28 

TASCFORM Summary 29 

Methodologies Summary 29 

Chapter 3: Variable Selection 31 

Structuring the Problem 31 

Defining Components 31 

Air Weapon Systems Subcomponents and Attributes ; 2 

Force Propagation Subcomponents and Attributes 33 

Variable Selection Guidelines $6 

Variable Selection Process 37 

Air Weapon Systems 37 

Airframes . . . 38 

Target Acquisition Systems 40 

Air-lo-Air Missiles 42 

Aerial Guns 43 

Relational Variables 44 

Force Propagation Variables 46 

Inventory 47 

- viii - 



Employment 47 

Summary 49 

Chapter 4: Data Collection 50 

Collection Boundaries 50 

Temporal 50 

Functional 50 

Informational. 51 

Some Collection Principles 51 

Leveling the Field 51 

Conflicting Evidence 51 

Resolving Contradictions 52 

Filling Gaps 52 

All the Numbers . 52 

Analogous Comparison 53 

Regression Analysis 53 

Estimative Analysis 55 

Expert Review 55 

Sources and Methods 56 

General Comments 56 

Airframe Performance Data 56 

Sources 56 

Comments 57 

Target Acquisition Systems 60 

Sources 60 

Comments 60 

Air-to-Air Missiles 61 

Sources 61 

Comments 61 

Aerial Guns 62 

Relational Variables. . 62 

Aircraft Configuration Data 62 

Relative Utilities 65 

Air Inventories 67 

Sources 67 

Comments 68 

Protest and Progress 70 

Chapter 5: Data Reduction 71 

Criteria 71 

Alternative Methods 71 



Single 'Marker' Variable 



7 



Composite Indices 73 

Factor Analysis - A Reprise 74 

Summary' 75 

A Minimalist Approach 75 

Variable Reduction 76 

Analyze or Assign 76 

The Airframe Example 76 

Target Acquisition Systems, Missiles, and Guns 7') 

Attribute Indices Utilization 80 

The Dilemma 80 

A Possible Resolution 80 

The Ratio Test 81 

The Distortion Test 82 

The Scale Test 83 

A Reduction Method 84 

Data Reduction Results 84 

The Airframe Subsystem 84 

Speed/Energy Attribute 85 

Maneuverability Attribute 86 

Air-to-Air Range Attribute 87 

Air-to-Ground Range Attribute 88 

- ix - 



Air-to-Ground Ordnance Attribute 89 

Detectability Attribute 90 

Target Acquisition Systems 91 

Air-to-Air Missile Subsystems 92 

Aerial Gun Subsystems 93 

Maintenance Force Quality 94 

Summary 96 

Chapter 6: Air Combat Potential Score Computation 97 

Air Weapon Systems 97 

Principles 97 

Airframes 98 

Target Acquisition Systems 99 

Weapons Payload 100 

Aerial Guns 100 

Air-to-Air Missiles I 1 ") 

Air-to-Ground Ordnance 1 < 1 1 

Full Payload 102 

Vulnerability 103 

Combining Subsystems 104 

Air Weapon System Results 105 

Air Defense Mission 105 

Fighter Mission 106 

Interdiction Mission . I 

Close Air Support Mission 108 

Force Propagation 109 

General Comments 109 

Available Inventory in Role 110 

Sortie Rates . . 110 

Sortie Production 112 

Combat Force Potential 

Summary 117 

Chapter 7: Policy Assistance Applications 118 

Criteria .••••: 1 1 S 

Enhancing Jordanian Air Combat Potential 119 

Aircraft Alternatives 120 

Force Structure Impacts 122 

Modifying Assumptions and Packages 1 23 

Alternate Assumptions 1 23 

Alternate Package Composition 124 

Assessing Regional Stability . . . 126 

Jordan and Allies Versus Syria 126 

Jordan and Allies Versus Israel 127 

Conclusions 129 

Other Applications 130 

Air Intelligence Analysis 1 30 

Operations Research/Analysis 131 

Microcomputer Processing 131 

Chapter 8: Summing Up 133 

Analytical Structure 1 33 

Data Collection 133 

Data Aggregation 134 

Results 136 

Evaluation 136 

Suggestion for Further Development 137 

Conclusion 137 



x - 



Appendix A: File Descriptions 139 

Middle East Combat Aircraft File 139 

Middle East Target Acquisition Svstem File 141 

Middle East Air-to-Air Missile File 142 

Middle East Aerial Gun File 144 

Middle East Air Weapon Svstem Configuration File 145 

Middle East Air Order of Battle 1984-1990 147 

Appendix B: Middle East Air Weapon Systems Data 148 

Airframes 148 

Target Acquisition Systems 159 

Air-lo-Air Missiles 161 

Aerial Guns 164 

Air Weapon System Configuration 165 

Appendix C: Aircrew Survey and Relative Utility Variables 171 

Aircrew Survey 171 

Survey Derived Relative Utility Values 174 

Appendix D: Middle East Air Orders of Battle 1984-1990 176 

Appendix E: Air Weapon Subsystem Factor Scores 1 83 

Airframes 183 

Target Acquisition Systems 1 86 

Air-to-Air Missiles 187 

Aerial Guns 188 

Appendix F: Combat Potential Scores Mideast Air Weapon Systems 189 

Air Defense Mission 189 

Fighter Mission 191 

Interdiction Mission 193 

Close Air Support Mission (CAS) 195 

Appendix G: Middle Eastern Air Combat Potential 1984-1990 197 

Bibliography 202 



FIGURES 

2.1 Airspeed Utility Curve 23 

2.2 Utility Function Curves - Range at Maximum Speed 25 

2.3 Composite Utility Curve - Range at Maximum Speed 26 

3. 1 An Analytical Typology: Air Weapon System Component 33 

3.2 An Analytical Typology: Force Propagation Component 35 



- XI 



TABLES 

2.1 Factor Analysis Of Combat Aircraft - Snider 14 

2.2 Factor Analysis Of Combat Aircraft - LeGrow 15 

2.3 Dimensions Of Air-To-Air Fighter Capabilities 17 

2.4 Dimensions Of Air-To-Air Fighter Capabilities 19 

2.5 Air Superiority Fighter Performance Components 23 

2.6 Fighter Utility Scores - Air Superiority 24 

3.1 Airframe Variables 38 

3.2 Target Acquisition System Variables 41 

3.3 Air to Air Missile Variables 42 

3.4 Aerial Gun Variables 43 

3.5 Aicraft Configuration Variables 44 

3.6 Relative Utility Value Variables 46 

3.7 Inventory Variables 47 

3.8 Sortie Generation Variables 49 

4. 1 Predicting Air Intercept Radius 54 

5. 1 Airframe Variables Factor Analysis 73 

5.2 Factor Analysis - 125 Combat Aircraft 77 

5.3 An Observable Data Set 81 

5.4 Adjusted Ratio Level Scores 81 

5.5 Impact of the Control Case on Rankings 82 

5.6 Airspeed/Energy Factor Scores 86 

5.7 Maneuverability Factor Scores 87 

5.9 Air-to-Air Range Factor Scores 88 

5.1 1 Air-to-Ground Range Factor Scores 89 

5. 12 Air-to-Ground Ordnance Factor Scores 90 

5. 13 Airframe Detectability Factor Scores 91 

5.14 Target Acquisition System Factor Scores 92 

5.15 Air-to-Air Missile Performance Factor Scores 93 

5.16 Air-to-Air Missile Vulnerability Factor Scores l )3 

5.17 Aerial Gun Rate of Fire Factor Scores 94 

5. 18 Aerial Gun Effectiveness Factor Scores 94 

5.19 Maintenance Manpower Quality Factor Scores 95 

6. 1 Aircraft With Highest Air Defense Potential 106 

- xii - 



6.2 Aircraft With Highest Fighter Potential 107 

6.3 Aircraft With Highest Interdiction Potential 108 

6.4 Aircraft With Highest CAS Potential 109 

6.5 Daily Sorties By Mission - 1988 113 

6.6 Comparative Force Potential - 1988 114 

6.7 Comparative Force Potential - 1988 116 

6.8 Combat Mission Potential - 1988 117 

7.1 Combat Potential in Air-to-Air Roles 121 

7.2 Combat Potential in Air-to-Air Roles - Revised 121 

7.3 Jordanian Air-to-Air Combat Potential - Options 123 

7.4 Jordanian Air-to-Air Combat Potential - Revised . 124 

7.5 Jordanian Air Combat Potential 124 

7.6 Jordanian Air Combat Potential - U.S. Support 125 

7.7 Jordanian Air Combat Potential - F-l's Re-roled 126 

7.8 Jordanian/Syrian Air Combat Balance - Allied Support 127 

7.9 Arab/Israeli Air Combat Balance 128 

7.10 Arab/Israeli Air Combat Balance - Depreciated 129 



xi u 



Chapter 1 
ARMS TO THE THIRD WORLD 



1.1 Introduction 



Arms sales are far more than an economic occurrence, a military relationship, or an arms 
control challenge - arms sales are foreign policy writ large. - Andrew J. Pierre in The 
Global Politics of Arms Sales. 

1.1.1 The Dynamics of International Arms Trade 

Few who read a newspaper or watch the evening news would contradict this observation. Arms sales or 
grants have become the linchpin of American security relationships with much of the Third World. They 
are the cement which holds the Camp David Accords together; they are the nose under the Middle East- 
ern oil producers' tent; and they are on the leading edge of efforts to blunt direct or indirect Soviet 
advances in the Third World. Arms sales have been pivotal in enticing Third World governments to 
switch superpower allegiances and in securing overseas facilities to support force projection requirements. 
Important to United States' international security policy, arms transfers are critical, in the absence of 
comparable economic allures, to Moscow's overtures to current or potential Third World allies. Most 
industrial nations, confronted with ever rising weapons system and imported energy costs, rely on large 
scale arms exports to maintain affordable economies of scale for their own indigenous weapons produc- 
tion. 

With the post-colonial diffusion of international power and the subsequent tattering of Cold War 
alliances, the Third World's demand for increasing quantities of high quality weapons has more than kept 
pace with the supply. Recognizing superpower reluctance to chance a direct confrontation over Third 

World conflicts, emerging regional powers have come to rely on weapons inventories rather than diplo- 

2 
matic assurances as the best guarantees of their own security. Threats to the security in the non- 
industrial world have mushroomed in the past forty years, further stimulating demand. By one estimate, 
three-quarters of the conflicts occurring since World War Two have taken place in the Third World, with 
inter and intra state wars producing over 15 million casualties. With the post-war profusion of new states, 
the potential causes of war have multiplied. The aggregate number of national frontiers to be contested 
ttttttttttttttttttttt 

For instance. Cahn and Kruzel observe that military exports are vital to sustaining British and French 
military production lines, with aerospace industries required to export at least half their production to 
remain afloat. See Cahn et al, Controlling Future Arms Trade, pp. 68-69. 

2 

See Pierre, The Global Politics of Arms Sales, pp. 275-280, for a thorough discussion of the current 
significance of arms transfers in international affairs. 

- 1 - 



3 
has increased geometrically, as have the other sources of inter and intra-state conflict. The genesis of the 

conilicts themselves is imbedded in a crosshatched web of intraregional rivalries, political instability, and 
ethnic hostilities and not in the availability of modern arms. Nonetheless, virtually all conflicts in the 
Third World have been fought with weapons supplied by the industrial nations. 

It is open to debate if the availability of modern weapons stimulates or suppresses the tendency to 
violent conflict resolution in the Third World. Indeed, compelling historical and theoretical arguments 
can be made on either side of the question. The timely transfer of arms to a threatened state can make 
war an unacceptably costly option for an aggressive neighbor. Conversely, a perceived arms buildup by a 
potential adversary can provoke a preemptive attack (e.g., Israel in 1967). Modern weapons systems 
possess range, mobility, and firepower attributes which magnify the lethality of combat once joined, but 
those same characteristics might also foreshorten its duration. Rarely do weapon systems alone dictate 
the outcome of Third World conflict. Long term results are more often the product of intangibles such as 
military morale, national cohesion and will, and combat strategy. This fact notwithstanding, the acquisi- 
tion of modern weapons is a preoccupying security concern of Third World leaders, and their unfetterred 
supply is the litmus test of patron constancy. For major arms suppliers, responding to Third World 
demands poses a devilish political, military, economic, and ethical dilemma. 

1.1.2 The American Dilemma 

The American body politic has long sought to harmonize the elements in the arms transfer quandary. 
The tenor of arms transfer policies in the Twentieth Century has run the gamut from virtually unbridled 
promotion to high-minded prohibition. In the mildly pacifistic and isolationist climate of the 1930's, the 
United States Senate's Nye Committee investigated international arms trade and drafted legislation (Neu- 
trality Act of 1935), which set up a governmental agency to control the sale of arms and required the 
President to apply an arms embargo against any countries involved in conflict. Spurred by the results of 
the Nye investigation and popular exposes such as Engelbrecht and Hanighen's The Merchants of Death, 
the British Labor Party spearheaded an eventually unsuccessful attempt to prohibit the private production 
and sale of arms by companies in the United Kingdom. 

Following World War II, the United States, France, and Great Britain undertook to forestall a 

weapons explosion in the Middle East through the formation of the Near Eastern Arms Coordinating 

Committee (1950), which was charged with implementing multi-lateral standards of restraint adopted in 

the Tripartite Agreement of the same year. The Committee was moderately successful in maintaining a 

quantitative balance in the flow of arms to Egypt, Israel, and Iraq, but became unworkable in 1955. when 

*r*f"f"t"f — t"f — t"f"~l — f"f — ! — f — f*"f"f"*f"f"f"f" 

3 

See Starr and Most, 'Patterns of Conflict', pp. 39-48 for additional conflict related data. 

A fast-paced account of early Twentieth Century attempts to curtail international arms traffic can be 
found in Sampson, The Arm's Bazaar, pp. 68-89. 

- 2- 



the Soviet Union entered the regional arms market. 

In a different political clime, the Nixon Administration viewed large scale arms transfers as a cost- 
effective vehicle for strengthening international political allies, creating surrogates whose military capabili- 
ties would preclude the requirement for direct American presence in unstable regions. Reacting neg- 
atively to the 'Nixon Doctrine', Congress attached the Nelson Ammendment to the 1974 Military 
Assistance Bill mandating Congressional notification and review of proposed arms packages in excess of 
$25 million. A more restrictive approach was adopted in the International Security and Arms Export 
Control Act passed in June 1976. It not only reaffirmed Congressional review but prohibited strictly 
commercial sales in excess of $25 million and proposed that annual aggregate sales should not exceed the 
dollar level reached in 1976. 'Arms controllers' on Capitol Hill had an enthusiastic ally in President Car- 
ter whose political and ethical sensibilities had prompted him to include the control of arms transfers as a 
plank in his campaign platform. The policy which he promulgated set quantitative and qualitative 
boundaries to the export of arms. He proposed a descending dollar limit on agggregate transfers, a pro- 
hibition of the insertion of new or significantly higher combat capabilities into a region, and a number of 
other measures which would have severely curtailed the role of the American government and arms pro- 
ducers in stimulating or responding to Third World demand for arms. 

The tenor of the Reagan Administration's arms transfer policy has been more aggressive, substituting, 

7 
to paraphrase James Buckley, 'a healthy sense of self preservation' for 'theology'. Intent and rhetoric 

aside, arms sales since 1981 have still been scrutinized and reigned in by a Congress suspicious of the effi- 
cacy of arms transfers and sensitive to domestic political pressures. With the exception of transfers to 
Israel and Egypt in compensation for the maintenance of the Camp David Agreement, no major arms sale 
has been approved without a lengthy, public, and at times vitriolic debate. The furor over the AWACS 
sale to Saudi Arabia was without equal in post-war history. Congressional opposition forced the Admin- 
istration to defer plans to upgrade Jordan's air defense capabilities and to abandon a program to further 
enhance Saudi Arabian air defense and ground attack capabilities. Most recently, a proposal to supply 
air-to-air missiles for fighters the Saudi's had purchased from the United States was the subject of fierce 
political controversy. 



ttttttttttttttttttttt 

See Kemp, 'Arms & Security', pp. 19-20; and Sherwood, The Out of Area Debate. 

The program to establish Iran and Saudi Arabia as the 'twin pillars' of security in the Persian Gulf 
after the withdrawal of British forces in 1970 stands as a case in point. 

7 

Quoted in Pierre, op.cit., p. 62. 

- 3- 



1.1.3 To Trade or Not To Trade 

While U.S. arms transfer policy has vacililated in 'Hamlet-like' fashion over the past 50 years, its applica- 

8 

tion in specific instances is a product of how key decision makers answer four questions. Does a partic- 
ular arms package promote regional stability or fracture it? Are prospective recipients suitable targets for 
patronage? Are immediate economic benefits to the supplier offset by the potential domestic economic 
impoverishment of less well-heeled clients? Can the widespread sale of arms be reconciled with the etliical 
principles and political orientation of the American public? Answering these questions is essentially a 
political process to which no omnibus analytical regimen can be reasonably applied. Analysis of the mil- 
itary dimension of a proposed weapons transfer is an integral component of that process. 

1.2 Military Analysis and Arms Transfer Policy 

1.2.1 The Role of Military Analysis 

Military analysis forms the nucleus around which other, less analytically tractable, considerations can be 
arrayed and is a mandatory element in each arms transfer proposal. The fact that the military aspects of 
an arms transfer constitute only a portion of the problem set does not derogate from the requirement that 
they be portrayed comprehensively and effectively. Indeed, testimony before any congressional commit- 
tee, supporting or opposing an arms package, is invariably accompanied by a spate of figures charting the 
impact of the proposed transfer on the military capabilities of the recipient and the regional military bal- 
ance. The assessment of the strictly military dimension of an arms transfer is not deterministic; neither is 
it insignificant. 

In this context, the role of transfer related military capabilities analysis is to provide a policy assis- 
tance' mechanism to national decision makers. Military analysis must consider the impact of a proposed 
transfer on U.S. force posture, costs, and employment plans. More poignantly, it must assess the rele- 
vance of the transfer to the regional security situation, answering two questions. How does a given trans- 
fer affect the recipient's force posture and war making potential? How do the resultant changes in military 

force structure affect the regional military balance? In answering these two questions, attention need be 

9 
paid not only to the quantities of assets involved but also to their capabilities in definable mission roles. 

Judgment is an essential component in arriving at these determinations, but the analysis of aggregated 

tablular data simply cannot be avoided in the production of a useable assessment. Once the subject of 

tabular data is introduced, eyes role skyward; the spectre of impenetrable models of suspect relevance 

descends. 

ttttttttttttttttttttt 

8 

The Shakespearian metaphor is borrowed from Harkavy and Neumann, The Lessons of Recent Wars 

in the Third World, p. 21. 

a 

Richelson et al, Arms Transfer Control Criteria, pp.6 1-62, 64-68. 

-4- 



1.2.2 Principles of Military Analysis 

Analytical obscurity and irrelevance can be averted if some of the guidelines espoused by the 
Comptroller General are followed. Even when applied at the aggregate level, a quantitative appraisal 
does provide a '. . . useful anatomical description of the extent to which . . . forces have improved or 
deteriorated relative to those of a putative enemy.' As a composite index, the aggregated model necessar- 
ily masks some relevant distinctions and sacrifices the effects of synergy among its component parts. Its 
linear mathmatical form and the inclusion of simplyfing assumptions make these losses inevitable. Thus, 
its output cannot be applied independently but must be integrated with substantive non-quantitative 
analysis before conclusions can be drawn. Not rigorously scientific despite superficial appearances of pre- 
cision, the output of a quantitative model is highly dependent on the variables entered into it, the 
assumptions made concerning them, and its mathmatical form. To be usefully applied, the model's input 
data must be valid and accesible, its assumptions explicit, and its workings transparent. Finally, expert 
judgment must play a key role not only in interpreting and leavening a model's output, it must also be 
embodied in the formulation of the model itself. 

From a substantive perspective, a militarily-oriented policy assistance model must comprehensively 
capture the essential combat related properties of the systems being analyzed. When the nature of modem 
weapons systems is regarded, the relative combat contribution of key subsystems (e.g., air-to-air missiles, 
radars) is essential in the determination of overall capability. The ability to compare combat potential 
within a weapon system category and across alternative mission areas is a necessary attribute, as is the 
requirement to aggregate combined weapon system capabilities at the force level. While aggregation 
inevitably compromises precision, the trade-offs need to be niinimized and explicitly defined. Similarly, 

the analytical procedures chosen must be scrutinized to determine their inherent proclivities to generate 

1 2 
systemic and random error within the context of analytical objectives. 



ttttttttttttttttttttt 

See USGAO, Models, Data, and War: A Critique of the Foundations of Defense Analysis, pp. 1-24, 
54-55, and 148, for a discussion of the application of aggrcnatcd quantitative models and the rules 
which should govern them in the defense analysis process. While the GAO studies focuses on U.S. 
defense policy making, its lessons are equally applicable to the arms transfer problem. 

See comment in Leiss et al, Arms Transfers to Less Developed Countries, p. 1 74, which asserts that 
associated weapons subsystems are the key features which '. . . distinguish the military end use of a 
modern fighter-bomber. The principle is just as legitimately extended to other classes of weapon 
system. 

This last set of principles is adapted from a list presented by Richclson et al, op. cit., pp. 83-86. 



11 



12 



1.3 Research Methodology 

1.3.1 Objectives 

Acceding to this list of demands is a tall order, infrequently met. Regardless, the need for a systematized 
military analysis tool to support arms transfer and international security decision making is well estab- 
lished. A myriad of quantitative assessment techniques have been developed over the past 25 years by 
governmental agencies, commercial entities, and academic groups to meet the demand. None has 
achieved universal acceptance. The goal of this research is to propose a militarily focused aggregational 
methodology which capitalizes on the ground already covered and which adheres as closely as possible to 
the spirit, if not the letter, of the idealized principles described above. While all of the principles merit 
rigorous application, four can be singled out as receiving particular emphasis in the evolution of this 
methodology. First, the derivation of input data and the internal workings of the methodology are trans- 
parent. The sources, characteristics, and validity of each data element are described, as are the processes 
to which they are subjected. While this feature prolongs the descriptive process, it permits informed 
judgment on the methodology's utility. Second, the judgment of weapon system and intelligence experts 
was sought at each phase of the development process and integrated into methodology design and opera- 
tion. Third, the focus throughout is on mission-specific combat output potential, not on the analysis of 
weapon system inputs. While inputs such as weapons inventories or system characteristics constitute 
necessary starting points, the combat capabilities which they engender are the determinants of military 
potential. Fourth, the limitations inherent in the methodology and the data which it considers are clearly 
identified to facilitate realistic integration of systemic outputs in subsequent case oriented analyses. 

Two additional considerations, inferred from previously identified principles, also warrant mention. 
Methodological transparency is essential but not sufficient. The user of a policy assistance tool must also 
be able to manipulate it to satisfy specific lines of inquiry, rather than just being presented with static 
results. Consequently, a research objective is to develop a methodology with which a potential user can 
interact, performing iterative (sensitivity) analysis under varying conditions, priorities, and assumptions. 
Finally, in those instances in which methodological simplicity conflicts with substantive accuracy or rele- 
vance, substantive concerns take precedence wherever possible. 

1.3.2 Limitations 

Within the framework of these overarcliing objectives, some practical limits need to be drawn. The 
essence of the analytical process is theoretically unconstrained to a specific region or weapon system cat- 
egory. For developmental purposes, application of the methodology was restricted to the Middle Fast, 

North African retdon. This region was the recipient of 55% of the dollar value of all arms shipments to 
ttttttttttttttftttttt 

13 

Twenty-two countries were included on the regional set: Algeria, Bahrain, Egypt, Ethiopia, Iran. 

- 6- 



the Third World in 1983, continuing the trend established in the mid-1970's. The countries of the region 
are among the relative handful in the Third World with sufficient financial resources or super-power 
patronage to acquire significant inventories of modern weapon systems. Additionally, virtually all major 
systems in their inventories, with the exception of Israel's, are acquired internationally, and the subject of 
security assistance to the region dominates arms transfer policy debates within the U.S. political system. 
Finally, the series of recent and ongoing conflicts in the area provide some limited data on the combat 
application of these weapons systems as well as suggesting a military development pattern for other 
regions potentially embroiled in protracted conflict. 

The investigation will also be limited to consideration of air weapons systems. Anthony Cordesman 
observes that airpower '. . . is the critical form of military power in the (Persian) Gulf, because of the 
regional geography, limited lines of communication, and the limited sustainability of ground forces. 
Another experienced military observer comes to the same conclusion but extends its application to the 
rest of the region, noting that the effective use of airpower will be the determining element in the first 
rounds of any future Middle Eastern combat. At a more practical level, aircraft transfers and invento- 
ries are highly visible, so relatively reliable data concerning them are readily available. Their visibility and 
cost propel them into the forefront of security policy concerns from both the supplier and recipient per- 
spectives, enhancing methodological relevance. Finally, aircraft are the category of weapon system in 
which the author has the most practical expertise, such as it is. It should be noted that, although the field 
of inquiry for development of this prototype has been narrowed considerably from the outset, the princi- 
ples underpinning it are extendable to other regions and weapons classes. 

1.3.3 Organization 

The basic philisophical groundwork laid, the remainder of the study will step through the elements 
involved in constructing a methodology for evaluating the military impact of air weapons transfers on the 
combat potential of Middle Eastern states and on the regional military balance. Chapter 2 will review 
some of the more salient techniques applied to the problem in the past, highlighting their advantages and 
disadvantages. Chapter 3 will propose a structure within which to conduct the analysis and identify its 
key elements. Chapter 4 outlines the data collection process, noting significant impediments and the 

methods used to surmount them. The procedure employed to reduce relevant data to analytically man- 

ttttttftftttttttttttt 

Israel, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Somalia, 
Sudan, Syria, the United Arab Fmirates, Tunisia, the Yemen Arab Republic, and the Peoples' Dem- 
ocratic Republic of Yemen. While not necessarily corresponding to a geopolitical definition of the 
region, this basket of countries is believed to capture its most interesting conflict and arms acquisition 
patterns 



14 

15 



Cordesman, The Gulf and the Search for Strategic Stability, pp. 484-488. 

Kemp, Arms and Security. p4. For an alternative view directed to the Third World as a whole, sec 
Elliot A. Cohen, 'Distant Battles'. 

- 7- 



ageable proportions is detailed in Chapter 5. Chapter 6 proposes a technique for combining input data 
into individual air weapon system and force aggregated combat mission outputs and displays selected 
results. Chapter 7 exercises the methodology in generating partial answers to potential arms transfer poli- 
cy related questions, and the final chapter identifies some conclusions regarding the methodology and its 
potential application in assisting policy development. Throughout, the reader is cautioned to be sensitive 
to the limitations of the system, as well as its capabilities. Like any analytical methodology, it can accu- 
rately represent only a few of the more important attributes of the phenomena being investigated and does 
not assume to '. . .mimic the real world exactly. 



ttttttttttttttttttttt 

See Pyles, The Dyna-METRIC Readiness Assessment Model, p. 31 

- 8- 



Chapter 2 
METHODOLOGIES REVIEW 

2.1 General 

Quantitative techniques have been employed extensively over the past 25 years to estimate the impact of 
arms transfers on recipents' military capabilities and regional military stability. In different ways, they 
have all been confronted by the same problems: the identification of significant variables, the collection of 
reliable data, and the reduction of data to a common plane of comparison. Too often, the last problem 
has been solved at the expense of the first and second. This section will review some of the tecliniques 
employed historically and evaluate their adherence to the criteria outlined in the previous chapter. 

2.2 Counting 'Dollars' 

The most common medium of arms transfer analysis has been the comparison of the economic data 
associated with the transfer, often in the context of regional and national defense expenditures. Mone- 
tary value is certainly not irrelevant. The trigger which activates the Congressional review process is, after 
all, a dollar amount. The two primary publications which catalog the international flow of arms, the 
Arms Control And Disarmament Agency's World Military Expenditures and Arms Transfers and the 
Stockholm International Peace Research Institute's World Armament and Disarmament Yearbook, devote 

much of their effort to establishing the valuation of individual and aggregated arms transfers. American 

2 
debates concerning arms transfers are often predicated on package values, at least at the popular level." 

Reducing arms transfers to a common dollar measure has considerable merit and historical prece- 
dence, but its utility in the military analytical role envisaged here is limited. There is no doubt that dollar 
measures capture some sense of the magnitude of a transfer or of the priorities of Third World states. 
However, the singular use of economic values as the basis for military analysis has two drawbacks. First 
and less significantly, the methodology through which transfers are valued is inconsistent and often 
ttttttttttttttttttttt 



1 

2 
3 



Richelson et al, Arms Transfer Control Criteria, review several of the more notable dollar based arms 
race models, pp. 16-47. 

Of course, a disconcertingly large proportion of all American policy debates revolve around cost rather 
than functional effectiveness. 

Cordesman convincingly contends that dollar to manpower ratios, for instance, arc valid indicators of 
the extent of force modernization and support infrastructure development in The Gulf and the Search 
for Strategic Stability, p. 496. Another study, Ilildebrandt's Military Expenditure, Force Potential, and 
Relative Military Power, employs an econometric methodology to translate military economic data into 
comparative power outputs. 

-9- 



opaque. If the contract price is used to value a transfer, intervening variables such as concessionary terms, 
offsets, and co-production arrangements influence the product, calling into question its reliability as a 
common frame of reference. The assignment of monetary amounts based on an estimate of the analo- 
gous value of unit cost establishes a more level measurement plane. However, even this approach suffers 
from a fatal flaw when applied to the assessment of military utility. There is simply insufficient correla- 
tion between the economic value of arms packages (or expenditures) and their military utility. The allo- 
cation of dollars among package elements varies greatly. Better than half of the dollar value of U.S. arms 
transfers to Saudi Arabia has been dedicated to infrastructure development, while virtually all of the dollar 
amount of transfers to Israel has purchased weapons themselves. Even if this hurdle is cleared, a more 

basic problem remains. The most carefully sculpted dollar estimate provides no indication as to the mis- 

7 
sion adaptability, operational capability, or potential combat output of the system which the dollars buy. 

The comparison of the economic value of arms transfers and military expenditures can legitimately detect 
trends and relative priorities at the systemic, regional, and national levels; but it fails to capture the mili- 
tary impact of weapons system transfers on national force structures and regional military balances. 

2 J Counting 'Beans' 

One often applied solution to the inadequacies of dollar based measures has been the tallying of the 
weapons they buy. Certainly, the tabulation of the numbers of weapons systems being transferred and the 
inventories into which they are introduced is an essential element in any military analysis. But is it suffi- 
cient? The weight of opinion suggests not. Weekly news magazines are replete with charts showing 
stacked symbols of various categories of weapon system; so are the briefing screens of many Pentagon and 
Congressional conference rooms. At one level of abstraction, categorical quantitative measures such as 
these do depict general trends and gross patterns of arms transfer and force development. The condensa- 
tion of discrete weapons systems into categorical totals makes for presentational simplicity and permits the 

o 

application of some statistical techniques against homongenized data sub-sets. However, for the type 
military analysis required to assist arms transfer policy makers, they are inadequate. The estimation of 
ttttttttttttttttttttt 

Laurance and Mullen, 'Assessing and Analyzing International Arms Trade Data', pp. 13-21. 

This technique is used by SIPRI in developing its arms flow figures. 

Cordesman, Jordanian Arms and the Middle Eastern Balance, pp.30-31. 



There is virtual unanimity among scholars investigating arms transfers on this point. See for instance. 
Richelson et al, op.cit. p. 2; Baugh and Squires, Arms Transfers and the Onset of War, p.8; Letss et al, 
Arms Transfers to Less Developed Countries, pp.29-31; arid Sherwin and Laurance, Using Data in 
Security Assistance Policy Making, pp. 80-82; among others. 

See Leiss et al, op.cit.. pp.35- 116, for various examples of systemic analysis conducted at the weapons 
category level. Also, Baugh and Squires, op.cit., pp. 8- 12; arid Lewis, 'Emerging Choices for the Sovi- 
ets in Third World Arms Transfer Policy, pp.30-31. 

- 10 - 



military utility (output) requires more finely grained data than is conveyed by the tabulation of the num- 
bers of a category of weapons (input) which a nation possesses or will receive. Under most categorization 
schemes, an F-5E and an F-15E would both be counted as supersonic aircraft. The failure to account for 
the immense differences in capabilities between the two would cripple any serious attempt at guaging their 
impact on national force posture and regional stability. 

More frequently, military and policy analysts concentrate on the analysis of weapons-specific inven- 
tories or transfer packages. Certainly more useful information is conveyed, but inventories alone provide 
a precarious perch from which to spring to any refined analysis of potential military output. A general 
impression of force posture can be estimated by considering the systems' respective roles and generations. 
In a vacuum, a listing of weapons tells us little about prospective combat output and its implications for a 
regional military balance. Phrased differently, reviewing inventories can determine if a force is being built 
up or if acquisitions just reflect a replacement of existing weapons. It does not indicate the thrust of a 
force's modernization or mission expansion. If the qualitative differentiation among weapons and their 
mission adabtability to the particular employment environment is not considered, any resultant quantita- 
tive analysis will fall woefully short of providing the policy maker with militarily relevant assessments on 
which transfer decisions can be predicated. As one researcher notes, '. . . a mere enumeration of peace- 
time inventories. . . does not constitute an analysis of military capabilities. The assessment of employa- 
ble military force structure and realistic regional balances demands a more sophisticated measurement 
technique, one that considers the combat relevant qualities of the systems, their effectiveness in an oper- 
ating environment, and the level of support a user can provide. Not only do the capabilities of the major 
systems themselves have to be considered, but also the contributions to potential combat effectiveness 
made by key subcomponents (e.g., missiles, radars). The upgrade of system components can often have 
nearly as profound an impact on the performance of a weapon as would its replacement. 

Clearly, the estimation of the military impact of weapon systems transfers requires a more sensitive 

and flexible technique. While the reduction of arms transfers to a common economic measure or their 

consideration by category provide common ground for aggregate analysis, neither conveys the specificity 

of militarily relevant information required to project potential combat output. Detailed inventory analysis 

provides more granular information, but similarly lacks the performance related detail to permit all but the 

most general and speculative of assessments. The inventory approach also suffers from the drawback of 

not having a common base on which relative combat potential can be measured among national forces. 

ttttttttttttttttttttt 

Richelson et al cite the consideration of these four acquisition patterns as being essential to the deter- 
mination of the a nation's force posture and its relevance to a regional military Balance; op.cit., p. 64. 

Epstein, Measuring Military Power, p. 131. Similar comments can be found in Sherwin and I au- 
rance, op.cit., pp. 82-83; Handel, 'Numbers Do Count', p. 259; Lciss ct al, op.cit., pp.1 17-124; Snider, 
Arabesque, p. 6; and others. 

- 11 - 



Attacking these inadequacies, several researchers have developed alternative approaches which encompass 
performance related attributes. 

2.4 Factor Analysis 

In the mid-1970's, various studies grasped upon factor analysis as a technique well suited to the task of 
synthesizing performance characteristics into aggregate measures of weapon system capability. The earliest 
of the applications aimed at isolating dichotomous dimensions of aircraft performance characteristics and 
then extracting relative values or scores for each weapon on those dimensions. The dimensions were 
assumed to represent categories of mission (e.g., offensive, defensive) the execution of which was closely 
associated with the characteristics which contributed most significantly to their definition. Later studies 
took a more refined approach and developed factor models in which multiple dimensions were extracted 
and related not to mission but to system performance attributes (e.g., maneuverability) the relative values 
of which could then be combined to represent outputs in given mission areas. No matter the orientation 
of the effort , the factor analysis based studies demonstrated the capability to condense values for multiple 
performance characteristics into commonly based indices which could be integrated into force level analy- 
ses. In this regard, factor analysis deserves further attention. 

2.4.1 Description 

Factor analysis is recognized as a general scientific method for analyzing data. Originally devised by 
Charles Spearman in 1904 as a method of simplifying the complex phenomena determining intellectual 
ability, it has been refined and adapted over the years to explore patterns of relationships among data, to 
determine the structure of data, to reduce and eliminate redundancy in data, and to define a functional 
unity for the transformation of multiple variable values to a common scale. As an exploratory tool, 
factor analysis uncovers underlying independent sources of statistical correlation among a body of input 
variables. Applied to data sets in which the relationships are unknown or only suspected, it defines a 
patterned statistical relationship attributed to an abstract underlying dimension. It falls to the researcher 
to categorize the Junctional essence of this underlying order or to suggest uniform causality. 

Without delving too deeply into the statistical operation of the factor analysis process, a brief discus- 
sion of its characteristics will facilitate evaluation of factor analysis based studies. Two aspects of the 
process will be touched upon here, extraction of factors and rotation to a terminal solution. A third, fac- 
tor score production will be treated later. Factors, or underlying dimensions, mav be extracted bv several 
ttttttttftttttttttttt 

Recent literature is replete with exhaustive discussions of the application of factor analysis to social 
and political science problems. The followinsj have been drawn on heavily in this capsule treatment: 
R. J. Rummel. Applied Factor Analysis and Understandinu Factor Analysis', Dennis J. Palumbo, 
Statistics in Political and Behavioral Science, Satn Cash Kacnigan, Multivariate Statistical Analysis - 
A Conceptural Introduction and Jae-on Kim and Charles W. Nfueller, Introduction to Factor Analysis 
and Factor Analysis 

- 12- 



methods, with principal components extraction the method used in all of the studies under evaluation. 
Principal components analysis ingests a data file comprised of any number of variables and the values for 
relevant cases on those variables. The factor procedure first isolates the combination of variables which 
account for more of the total variance in the entire data set than any other combination of variables. This 
first component, or factor, represents the most inclusive summary of the linear relationships among the 
input data. A second component is then extracted which defines the second best variable combination 
and which accounts for the proportion of the variance not captured by the first. Thus, the second com- 
ponent or factor is orthogonal (i.e., at right angles) to the first. The process continues until sufficient fac- 
tors have been extracted to account for the total variance in the data set. A 'loading' is generated for each 
variable on each factor which measures the degree to which the variable is involved in the factor. In other 
words, a variable loading represents the correlation coefficient between the variable and a given factor. By 
comparing loadings for all factors and variables, the researcher can identify those variables most closely 
associated statistically with a particular factor or multiple factors. 

The initial factor solution is not unique, since other statistically equivalent combinations could well 
define a different array of underlying dimensions. Rotation to a terminal solution overcomes this uncer- 
tainly by mathmatically rotating the factor matrix to delineate distinct clusters of interrelated variables. 
Two rotational methods are commonly employed. Orthogonal rotation maintains the right angle separa- 
tion between the vectors which best fit distinct variable clusters. Oblique rotation does not require that 
the factors be uncorrelated with each other and more precisely defines cluster boundaries. 

2.4.2 Factor Analyzing Air Weapons Systems 

2.4.2.1 Defining Factors 

The earliest efforts to apply factor analysis to the evaluation of air weapons systems capabilities were 
launched by Michael Mihalka, Lewis Snider, and Allan LeGrow. While each study had its unique 
aspects, the similarities among them allow their discussion as a group. Mihalka and Snider hypothesized 
that fighter aircraft would fall along two dimensions. Mihalka defined these as 'attack' and 'defense', Sni- 
der as 'interception/air superiority' and 'tactical support ground attack'. Each selected variables (5 and 12 
respectively) which he suspected would define one dimension or the other. True to form, the analysis 
defined the expected dimensions. The results of Snider's inquiry, wliich considered 162 aircraft, are 
depicted in Table 2.1, with some editorial changes. 



tttttttttttttfttttttt 

12 

Mihalka, Understanding Arms Accumulation; Snider, Arabesque; and I,eGrow, Measuring Aircraft 

Capability for Military and Political Analysis 

- 13- 



Table 2.1: Factor Analysis Of Combat Aircraft ■ 


Siucler 


VARIABLE 




FACTOR 


1 


FACTOR 2 


Production Year 




. 78 




. 18 


Primary Mission 


Speed 


. 93 




. 13 


Maximum Speed 




. 98 




. 11 


Service Ceiling 




. 88 




. 00 


Thrust 




. 88 




. 20 


Rate of Climb 




.86 




-.02 


Take-off Weight 




.21 




. 74 


Payload 




.22 




. 76 


Ferry Range 




-. 01 




. 91 


Combat Range 




. 10 




. 91 


Radius -Internal 


Fuel 


. 13 




. 90 


Radius -External 


Fuel 


-.07 




. 86 



Reviewing the factor loadings, the variables group around those factors which correlate to the most desir- 
able capabilities for the respective missions, when Factor 1 is considered the air-to-air mission and Factor 
2 the air-to-ground mission. However, an argument can be made that the selection of variables for analy- 
sis turned the process into a self-fulfilling prophecy. In particular, regard Factor 2. Three of the variables 
(combat range, and the two combat radius variables) tap essentially the same characteristic with only 
minor variation. A similar situation exists between ordnance payload and maximum takeoff weight. Not 
only does this mode of variable selection tell us little more than we knew about the weapons system mis- 
sion adaptability coming in, the asymetrical representation of a functional attribute in this fashion can 
severly distort the solution. More importantly, the gerrymandering of input variables produced some 
suspicious relative factor scores on each dimension. Soviet SU-7's and SU-20's, which are single purpose 
ground attack aircraft with relatively short legs and high top speed capabilities, scored most highly on the 
air-to-air dimension, while the F-4E outpaced the F-14 on the same attribute. These results were artfully 
rationalized, but the point remains that key mission-related performance variables were eliminated from 
consideration not on the basis of functional merit, but because they did not correspond to a predeter- 
mined typology. 

LeGrow ascertained this deficiency and added variables to the data set which attempted to capture 
the effect of weapons on mission capability (number of gun barrels, missile algorithm). He also eliminat- 
ed the most redundant variables from the previous set and added ones with more aeronautical relevance 
(thrust-to-weight ratio and wing loading). Analyzing 29 aircraft, he extracted three factors, as shown in 
Table 2.2. 

ttttttttttttttttttttt 

13 

Rummel, Applied Factor Analysis, p. 21 1. 

- 14- 



Table 2.2: 


Factor Analysis Of Combat Aircraft - LeGrow 




VARIABLE 




FACTOR 1 


FACTOR 2 


FACTOR 3 


Maximum Speed 

Ceiling 

Thrust 

Rate of Climb 




. 91183 
. 90017 
. 81375 
. 85771 


-. 16005 

-. 14516 

. 33873 

-. 17088 


. 15425 

-. 10637 

. 27959 

. 31275 


Take-off Weight 
Payload 
Combat Range 
Combat Radius 




. 62739 
-.22243 
-. 06186 
-. 09686 


. 68222 
. 91291 
. 90778 
. 90804 


-. 04521 
. 07798 
.01947 
. 00532 


Thrust-to-Weight Ratio 
Wing Loading 
Gun Barrels 
Missile Algorithm 
Production Year 


. 54453 
. 07857 
. 07818 
. 30709 
.27103 


-. 32122 
. 34959 
. 13349 
. 24849 
.40090 


. 54158 
-. 83717 
. 88188 
. 52984 
. 52844 



Reviewing the results, LeGrow noted that the presence of a third factor complicated interpretation and 
that the elimination of redundant variables and the insertion of other combat relevant attributes produced 
an overall matrix in which the distinctions were no longer as clearcut. For instance, thrust-to-weight ratio 
loaded moderately on Factors 1 and 3, while several others (e.g. production year, wing loading, thrust) 
loaded heavily on one variable and moderately on others. LeGrow postulated that the combination of 
Factors 1 and 3 appeared to best represent air combat capability, with Factor 2 capturing air-to-ground 
qualities. While the combination of scores on Factors 1 and 3 produced performance rankings which 
were intuitively reliable, the scores generated for the second factor contained some serious anomalies. The 
F-16, which has a significant ground attack capability, ranked below the F-5E on that factor, while the 
F-14A, an interceptor, was exceeded only by the A-6E and the A-7D. To further test the procedure, 
LeGrow considered only aircraft with an air-to-air mission and reduced the number of variables in a sec- 
ond factor problem. Again, three factors emerged, but with different and functionally contradictory vari- 
able loadings. Regarding LeGrow's results, the volatility of the factor analysis process becomes clear. 
The alteration of variables or cases can produce drastically different dimensions, some of which are not 
easily abstracted to higher order concepts such as mission output. As he also pointed out, the combina- 
tion of multiple factors to produce a mission score is an arbitrary process if only factor analytic results are 
considered, 
ttttttttttttttttttttt 



14 



The author believes that LeGrow's third factor would have decomposed into two factors had he 
considered a larger number of cases. One factor would have been defined largely by the weapons 
related variables, the second by thrust-to-weight ratio and wing loading (negative loading). Test runs 
on a data base with 86 aircraft tended to confirm this estimate. Thrust-to-weight ratio is directly 
related to maneuverability, and wing loading is related to it inversely from an aeronautical perspec- 
tive. 

- 15- 



The Analytical Assessments Corporation's (AAC) study team, which included Lewis Snider, applied 
a more sophisticated factor analytical methodology to the problem. Most importantly, they increased the 
number and aeronautical relevance of the variables under analysis and defined factors which purported to 
represent system attributes rather than combat mission outputs. The study aimed to use factor analysis to 
determine dimensions of fighter capabilities which would be invariant' regardless of minor alterations in 
variable selection, case compostion, or rotation technique. Initially, all aircraft were factor analyzed in a 
single model. Explaining the at times unrealistic results produced some inventive but aeronauticaily spe- 
cious formulations. The analytical problem was consequently segmented, with separate analyses con- 
ducted for interceptors and air superiority fighters and for ground attack and close air support aircraft 
respectively. Aircraft were treated both as 'launch platforms' (internal weapons only) and as full weapons 
systems (external ordnance included). Delineating mission groupings prior to analysis averted many of the 
interpretation problems and spurious results which confronted Snider and LeGrow. It also permitted the 
independent analysis of multi-role fighters in each mission area. Furthermore, distinct analyses were 
accomplished for air-to-air and air-to-ground missiles, the results from which were integrated into the 
overall air weapon system model. The result was a smorgasbord of analytical options. 

One data set and model will be discussed here. It analyzes interceptors and air superiority fighters as 
weapon systems with capability scores for air to air missile systems included. This analysis was selected 
because it is the most sophisticated of variable combinations evaluated which also vividly illustrates the 
pitfalls of attempting to stretch a technique past its limits. Fifteen variables observed for 69 interceptor 
and air superiority fighters were analyzed, with five factors extracted. The names assigned these factors 
and the variable loadings derived are depicted, with minor stylistic editing, in Table 2.3. Only loadings of 
0.5 or higher are shown to highlight the factors. 

Before discussing the results, some observations on the variables themselves are warranted. First, 

year of production is intended as a surrogate representing relative technological sophistication or moder- 

17 
nity. While this contention is superficially pleasing, its underlying assumption is invalid. Consider, for 

instance, three U.S. aircraft, all of which were flown for the first time within four months of each other in 

1972. The F-15 is a leading-edge high technology fighter; the F-5E is a considerably less sophisticated 

export aircraft; and the A-10A is a technologically austere ground support lighter. When aircraft have 

different design and cost goals, knowing the year of production conveys little as to their relative techno- 

ttttttttttttttttttttt 

See the convoluted explanation as to why the F-14 scored lower than the F-5 as an interceptor air 
supenority fighter as an example, pp. 123- 124. 

In all 18 analyses were conducted at the air weapon system level, with six for missiles. Factor rota- 
tion techniques were varied to control for systemic bias. These are presented in toto in Richelson et 
al, op. cit., pp. 144-192. 



17 



The same variable was also used by Snider and LeGrow. 

- 16- 



logical sophistication. The materiality of the variable diminishes even more when generational compari- 
sons are made between aircraft produced by different nations, whose own technological capacities are far 
from even at the same point in time. Secondly, the variable 'Mission Potential' was constructed by mul- 
tiplying the combat radius of an aircraft by its mission speed. Intended to illustrate the point that high 
speeds can reduce combat endurance, the combinational form has no aeronautical precedent and ignores 
the fact that mission speed is one of the factors, along with ordnance load and flight profile, which is 
involved in the determination of combat radius in the first place. Third, the 'missile guidance' variable 
was derived from a separate factor problem in which the attribute was described by two dichotomous 
variables, 'infra-red guided' and 'semi-active radar homing guided', which were assigned nominal valua- 
tions (0 or 1). Logically, these varied inversely for any given case, defining a factor with high (.98) posi- 
tive and negative loadings. In the factor scoring process, which will be described below, the dichotomous 
loadings cancelled each other out producing 'missile guidance scores' which were predicated on the values 
for all variables except the guidance value. 



Table 2.3 


Dimensions Of Air-To-Air 


Fighter Capabilities 




VARIABLE 


ENERGY/ 
TECH- 
NOLOGY 


WEAPONS 
SUITE 


ARMA- 
MENT 


ENDUR- 
ANCE 


MANEUVER- 
ABILITY 


Production Year 
Rate of Climb 
Combat Ceiling 
Combat Speed 
All Weather 
Payload 


. 75280 
. 94426 
. 79378 
. 91804 
. 50267 
. 90748 










Mission Potential 
Combat Radius 


. 70984 






. 54982 
. 96576 




Thrust to Weight 
Wing Loading 


. 71315 








. 89728 
-. 54015 


Muzzle Velocity 
Rate of Fire 






. 97935 
. 98072 






Msl Lethality 
Msl Envelope 
Msl Guidance 




. 89930 
. 87492 
. 86691 









Glancing at Table 2.3, the effects of these variable selection anomalies can be seen. Mission potential 
loaded significantly on the energy and endurance factors, a predictable situation since the variable was 
created by multiplying combat radius times combat speed. Otherwise, the results arc largely non- 



- 17- 



contentious, showing predictable statistical affinities among variables. The missile and gun variables 

define factors representing the air-to-air missile suite and gun armament respectively. Wing loading shows 

1 8 
a negative relationship to the maneuverability factor, as it should. However, wing loading also has an 

even higher positive loading on the energy/technology factor, an observation requiring clarification. While 

the resultant variable groupings could have been postulated intuitively, the addition of the statistical 

dimension offers the opportunity to create multi-variable indices which reflect the relative capability of 

each aircraft on each combat related attribute. 

2.4.2.2 Extracting Factor Scores. 

The key utility of factor analysis in this context is its ability to generate scores for each case on the 
underlying dimension or factor. Unfortunately, its promise fades when it is employed in tlus role at the 
air weapon system level. The scoring process entails two salient features. The absolute values of all vari- 
ables in the set weighted proportionately to their involvement (positively or negatively) in the factor are 
considered in the solution and are summed to yeild the factor score for a case. The operative assumption 
is that each factor is a linear combination of the case values for every variable in the problem set. Thus, a 
variable which is largely unrelated statistically (and perhaps not at all functionally) to a factor has a defi- 
nable impact on the score. Secondly, the absolute values for the variables are converted to standardized 
scores with a mean of zero and a standard deviation of one before the scores are rendered. Consequently, 
some scores are negative values even when all variables load positively on the factor; and all scores are 
measured on an interval scale. 

From a technical perspective, the factor score coefficient matrix (F) is derived from the rotated pat- 
tern matrix (A) according to the formula: 

F = (A T A) _1 A T 
Score coefficients are consistent with the weight and direction of the factor loadings. Variables with high 
factor loadings receive higher score coefficients relative to their loadings within the confines of the entire 
problem set. Weaker loadings produce coefficients which tend toward zero, and negative loadings generate 
negative coefficients. A factor score (f) is then developed for each case by summing the products of the 
factor score coefficients (F) of all variables in the factor problem and the standardized values of each case 
(z) on those variables. In equation form, the factor score for a case (fi) in a three variable factor problem 

would calculated bv the equation: 
ttttttttttttttttrtttt 

18 

In earlier tables which did not include the missile variables, wing loading loaded positively on the 

factor asserted to represent maneuverability, a questionable relationship aeronautical!) - . 



19 
20 



If the alternative regression method of extracting score coefficients is used, tests indicate variables 
with the weakest positive loadings will also be awarded negatively signed score coefficients. 

This description and equations are adapted from the examples offered in Nie et al Statistical Package 
for the Social Sciences Second Edition, pp. 487-489. The formulae cited apply to factors extracted by 

- 18- 



f l ~ F varl z l + F var2 z 2 + F var3 z 3 
The problems stemming from the first characteristic can be deduced from a review of the data in 

Table 2.4, which is an unblanked version of Table 2.3. 



Table 2.4 


Dimensions 


Of Air-To-Air Fighter Capabilities 




VARIABLE 


ENERGY/ 
TECH- 
NOLOGY 


WEAPONS 
SUITE 


ARMA- 
MENT 


ENDUR- 
ANCE 


MANEUVER- 
ABILITY 


Production Year 
Rate of Climb 
Combat Ceiling 
Combat Speed 
All Weather 
Payload 


. 75280 
. 94426 
. 79378 
. 91804 
. 50267 
. 90748 


-. 18566 

-. 11167 

. 36669 

. 01481 

-. 37841 

.04379 


. 36325 

. 03775 

-. 31621 

-. 07273 

-. 48219 

. 04384 


.46583 
-. 16755 
. 08626 
. 16637 
.47030 
. 24689 


-. 19166 
. 07555 
. 28050 
. 24171 
. 25789 

-. 07155 


Mission Potential 
Combat Radius 


. 70984 
. 13473 


. 14295 
. 15801 


-. 19642 
-. 10303 


. 54982 
. 96576 


. 24850 
. 06441 


Thrust to Weight 
Wing Loading 


.21866 
. 71315 


-. 29951 
-. 32802 


. 11426 
. 01955 


. 09139 
. 11413 


. 89728 
-. 54015 


Muzzle Velocity 
Rate of Fire 


-.02070 
.00287 


-. 11725 
-. 14115 


. 97935 
. 98072 


-.03979 
-. 09275 


. 13076 
-. 00497 


Msl Lethality 
Msl Envelope 
Msl Guidance 


. 18269 

.07271 

-. 10591 


. 89930 
. 87492 
. 86691 


.03031 
-. 33946 
-.09772 


-. 25055 
. 32456 
. 21867 


-. 12790 
-. 00899 
-. 30998 



Looking at the factor which allegedly captures air-to-air missile capability, the missile performance vari- 
ables load positively. However, all-weather capability has a moderate negative loading, as does thrust-to- 
weight ratio. Thus, the score for a missile mounted on an technologically superior aircraft would be less 
than the score derived for the same missile mounted on an inferior platform. This scoring quirk is partic- 
ularly nettlesome when one considers that all radar guided missiles are dependent on an air-intercept radar 
(an attribute of an all-weather system) for their guidance. A similar relationship prevails for gun effec- 
tiveness, the score for which would be diminished by the value of an aircraft's all-weather capability, 
combat ceiling, missile launch envelope and others. Scores for the maneuverability attribute would be 
diminished as a result of a later production year (modern technology surrogate) while being enhanced by 
the presence of an all-weather radar and lessened if assigned missiles had more capable guidance systems. 



ttttttttttttttttttttt 



21 



principal components analysis. 

If the weak negative loadings for two other energy/technology variables, production year and rate of 
climb, are considered, the situation deteriorates further. 

- 19- 



Observations of this type could be made indefinitely. The essential point is that factor scoring con- 
ducted at the weapon system level forces the inclusion of functionally irrelevant data in the computation 

of values for discrete attributes. A defense of this characteristic has been advanced which contends that it 

22 
captures the tradeoffs which must be made between some attributes in aircraft design. While this con- 
tention might seem logical in a very narrow sense (e.g., maneuverability or speed being reduced to permit 
greater payload in a similar generation of aircraft), it ignores the advances which permit simultaneous 
improvements in multiple attributes. More poignantly, it is largely invalid when applied across subsys- 
tems, many of which are aircraft non-specific and which are developed independently of each other. Most 
U.S. aircraft can carry a version of the AIM-9 and are fitted with an M61A1 cannon. The two subsys- 
tems are technologically unrelated, and any scoring system which diminishes the value of one because of 

23 
the presence of the other is flawed. 

The flaw in the 'vertical' (i.e., intra-factor) scoring process has a horizontal analog. The AAC study 
and others compute total system capability as an unweighted linear combination of factor scores denomi- 
nated by the number of factors involved. Consequently, the value which describes the capability of the 
aerial gun has the same relative weight in the computation for air-to-air effectiveness as does energy or 
maneuverability. Not only is this supposition counterintuitive, it is roundly contested by the results of an 
aircrew survey that established that an aerial gun has a relative utility of .067 in an air superiority role and 
.043 in an interception role. An unweighted linear computation of factor scores overrepresents the role 
of the gun by more than 200%. The combined influence of these two scoring traits produces relative val- 
ues at the air weapon system level which obscure more than they illuminate. 

2.4.2.3 Using Factor Scores. 

The mathmatical process by which factor scores are measured presents another, although far less intimi- 
dating, problem. Because factor scores are computed on a standardized scale, some have negative values. 
While these values accurately portray the distance between cases and can be used in direct comparisons of 
cases on a given factor, they are not conducive to further combination. Earlier researchers attacked the 
problem by adding a constant to the set of scores which raised the lowest negative score to a desired 
threshold (e.g., 0.1 or 1). LeGrow demonstrated that the use of a constant in this fashion preserved the 

interval relationship among the scores but distorted their ratio relationship. While the implication that a 

tttttttttttttttttfttt 



22 

23 

24 



See Snider, op.cit., p. 55, for one such assertion. 

A statistical consideration concerning subsystems is also relevant. Since the input variable values for 
any aiven subsystem would be entered multiple tunes reflecting their fitting to several aircraft, they 
would constitute what Rummel terms an 'a priori' factor, detracting from the patterned variation 
essential to the derivation of meaningful factor groupings. 

Supporting survey results, seven percent of the Israeli air kills over Lebanon in 1982 were aclucved 
by gun shots. See, Lambeth, Moscow's Lessons from the 1982 Lebanon Air War, pp. 10-11; and 
Cams, 'Military Lessons of the 1982 Israel Syria Conflict', p. 268. 

- 20- 



valid ratio relationship existed in the fust place was incorrect, the observation that the addition of a func- 
tionally irrelevant constant created a pseudo-ratio relationship of arbitrary significance stands. 

The AAC study took a more elaborate approach to raising negative values above zero by applying 
the expression for calculating a T-score (10*Z+50) to the raw factor score but acknowledged that the 
transformed scores still lacked true ratio properties. Consequently, the ratio of capabilities between two 
systems could only be inferred. Some examples were offered which asserted that meaningful comparisons 
between alternate weapon systems packages could still be made as long as the limitations of the data were 
recognized. 

2.4.2.4 Factor Analysis Summary. 

Factor analysis constitutes a powerful tool for reducing large bodies of data to statistically valid composite 
indices. Applied to the evaluation of combat aircraft, it produces results which do not always embody a 
commensurate degree of operational validity. As demonstrated above, comprehensive variable selection is 
crucial, and factor results can prove erroneous if the variables considered do not represent the bulk of a 
system's aeronautically and operationally relevant attributes, to include those of its subsystems. Addi- 
tionally, factor results are sensitive to relatively minor variations in variable and case composition, so their 
ability to define 'invariant' dimensions for fighter performance over differing spatial and temporal domains 
is suspect. The extrapolation of the raw factor analysis output to operationally pertinent composite indi- 
ces is crippled by three characteristics when applied at the system level. Functionally irrelevant informa- 
tion is included in generating factor scores. The combination of scores for multiple factors into a com- 
posite is arbitrary and often produces illogical results. Finally, the composite indices created from factor 
outputs are interval level measures which lack the mathmatical properties to permit their aggregation at 
the force level. 

2.4.3 Multi-Attribute Utility Theory 

To overcome several of these deficiencies and to account for intangibles such as operator proficiency and 

support capability, LeGrow explored three alternate techniques for creating composite indices of fighter 

capabilities: paired comparisons, successive intervals method, and multi-attribute utility theory (MAUT). 

After experimenting with each, he concluded that MAUT was the '. . .only technique comprehensive 

enough to deal with capability as more than just a combination of performance characteristics.' Follow- 

ing his lead, Lowell Jacoby applied MAUT to an assessment of ship sea denial capabilities. The fact 
ttttttttttttttttttttt 

25 

See Richelson et al, op.cit., pp. 218-220 for a discussion of methods of dealing with the level ol 

measurement problem. While this author has no quarrel with their methodology, he takes exception 

to their contention that interval nature of factor scores is the 'most serious drawback' to their use at 

the systems level. 

See LeGrow, op.cit., pp. 119-137 and Jacobv, Quantitative Assessment of Third World Sea Denial 
Capabilities, pp.58- 154. The discussion of MAUT here is taken from these two publications and 

- 21 - 



that MAUT permits the consideration of multiple variables, produces ratio measurement scales, and 
involves expert judgment in defining combinational rules marks it as having significant promise in the 
analysis of air weapon system capabilities. 

2.4.3.1 Description. 

MAUT is a general approach for combining the utility values of multiple attributes into a single measure- 
ment of utility under a specified set of circumstances. A panel of experts is requested to develop a scale 
for each variable which reflects the relative utility of the variable's absolute values in a given scenario. 
Through this process, the absolute values of multiple variables are transformed to a common measure- 
ment scale (utils). Each util scale runs from to 1. As the first step in the development of the utility 
function curve, judges are requested to identify the absolute value at which the variable under considera- 
tion has no utility and the absolute value at which its utility in the postulated scenario peaks. These 
absolute values anchor the opposite ends of the utility function curve. Judges are then requested to match 
successive increments of change in a variable's absolute value above the lower anchor point to corre- 
sponding increases in utility up to the maximum useful value which is assigned a utility score of 1. A 
utility curve is constructed by connecting these discrete points. Through this procedure, a 'natural' zero 
point is established, and the utility scores are assumed to have ratio properties. The absolute value for 
each variable is converted to a util value by imposing it on the respective utility function curve. Their 
values now transformed to commonly based ratio measures, the variables can be combined to define the 
relative value of multi-variable attributes and multi-attribute systems. 

The combinational rules which govern aggregation at the attribute and system levels are also the 
product of expert judgments as to the relative importance (weight) of the attribute's or system's compo- 
nents. The technique assumes that the experts will make rational choices in developing utility scales and 
identifying combinational weights, seeking to maximize expected gains and minimize expected losses at 
each step in the process. Effective application of the technique is dependent on a clear statement oi the 
inquiry's purpose and operative scenario, the selection of variables which capture the pertinent aspects of 
the phenomena under investigation, the expertise of the judges, and their access to sufficient information 
concerning the variables, attributes, and systems which they are evaluating. 

2.4.3.2 Application 

To test the theory, LeGrow devised a scenario to score fighter aircraft in a Middle Eastern air superiority 
engagement. lie identified three relevant components and the variables which defined them. These are 
shown in Table 2.5. 

ttttttttttttttttttttt 

from critiques contained in Richelson et al, op.cit., pp. 88-101, and Sherwin and Laurance, op.cit., 
pp. 95-101J. 

- 22- 






Table 2.5 


: Air Superiority 


Fighter 


Performance Components 


PLATFORM 




PAYLOAD 


Maximum Speed 
Thrust to Weight 
Wing Loading 
Combat Radius 


Ratio 


Missile 
Missile 
Firing 
Number 


. Range 
Speed 
Envelope 
Guns 




EMPLOYMENT FACTORS 






National T 
National P 


echni 
ilot 


cal Capacity 
Proficiency 





A two judge panel devised utility function curves for each variable and specified weightings for each within 
its component. A sample utility curve for maximum speed is shown in Figure 2. 1 



Figure 2.1: 


Airspeed Utility Curve 








1.0 1 










.75 ■ 








u(x s ) 


.5 • 












.25 



















1 




1 


.0 1.2 1.5 1.8 


2.0 


2.5 




MAX COMBAT MACH NO 







Regarding this curve, an application question arises. While there is no doubt that speeds in excess of 
Mach 1.8 are of diminished utility in air superiority engagements, would an aircraft with the technical 
potential to exceed Mach 1.8 then be assigned a lower utility score derived from the downward sloping 
end of the curve? From the scoring tables in the Appendix, it appears that this was the case. If so, the 



- 23- 



score extraction ignores the fact that an aircraft which has a maximum speed capability of Mach 2.5 can 
also usually operate at Mach 1.8. The same problem also appears to affect the extraction of utility scores 
for the range value. One other problem area emerged in reviewing LeGrow's individual utility curves. 
The utility function for national technical capacity was developed with a list of countries along the y-axis 
which were then assigned utility values. With no absolute measures of technical proficiency to govern the 
assignment of utility values, the utility function curve was defined by intervals between the countries 
arrayed at the bottom. The approach appears to be a misapplication of the utility concept, since the 
cost-benefit rationale which is supposed to govern curve development is abrogated. In a broader perspec- 
tive, MAUT does not appear adaptable to the analysis of problem sets which include nominally or ordi- 
nally measured variables. 

These observations aside, LeGrow combined the extracted utility values in accordance with the 
intervariable weightings assigned by the judges and then multiplied the platform and payload sub-totals to 
generate a final weapon system score independent of country. The aircraft and their utility values are 
depicted in Table 2.6. 



Table 2.6: Fighter 


Utility 


Scores - Air Superiority 


AIRCRAFT 






WEAPON SYSTEM UTILITY 


F-16 






. 96 


F-15 






. 86 


F-14 






. 79 


F-4E 






.47 


F-5E 






.48 


Mirage 


IIIC 


. 69 


MiG-19 






. 68 


MiG-21 






. 62 


MiG-23 






.58 



Unfortunately, utility scores show some of the same vagaries that plagued factor scores. The utility value 
for the MiG-19 identifies it as more capable than all fighters except the latest U.S. fighters and the Mirage 
IIIC and almost 50 percent more capable than the F-4E. The F-4E sits lowest in the group, a ranking 
not merited by its weapons suite or combat avionics. Three factors seem to have forced these unsuitable 
results: insufficiently comprehensive variable selection, the above noted scoring idiosyncracy. and using a 
multiplicative combinational technique at the system level. 

While Jacoby's study considered sea denial ships rather than aircraft, a partial review of his findings 
illuminates some other features of the mult-attribute utility technique. Proceeding from LeGrow's 



- 24- 



exploratory effort, Jacoby launched a full-fledged MALT inquiry. Most significantly, he employed mul- 
tiple independent judges to enscribe the initial utility function curves rather than tasking two judges to 
develop consensus curves. The profound differences of opinion among 1 1 judges concerning one variable, 
range at maximum sustained speed, are exhibited in Figure 2.2. Similarly fragmented results were 



obtained for virtually every variable (15) in the problem set 



27 



Figure 2.2: 




Utility Function Curves - Range at Maximum Speed 






7.5 


II /w ""t 4 




m 

3 

< 
> 

> 
3 


5.0 


'\ll// X 






2.5 




' i * — -—i > i ' 






°0 500 1000 1500 2000 2500 






RANGE AT MAXIMUM SUSTAINED SPEED |nm| 



This significant and largely unpatterned variation in responses presented an interpretation and appli- 
cation challenge and illustrates one of the drawbacks of employing MALT in this type of investigation. 
Jacoby tested two methods for condensing multiple utility assessments. Just one will be discussed to 

illustrate the problem. One alternative is to mathmatically synthesize a single utility function curve from 

28 
the curves described by the judges. He found tlus technique to be fraught with mathmatical complexity 

and prone to error. For illustrative purposes, the composite utility function curve derived from the curves 

depicted in Figure 2.2 is shown in Figure 2.3. 

ttttttttttttttttttttt 



27 
28 



Jacoby also considered the same variables under two different employment scenarios, causing each 
judge to create as many as 30 utility function curves. 

The other method is to score each case on each of the initial utilitv curves, sum the results, and 
determine an average utility score. This is the method finally used by Jacoby. I ike many of the 
MALT-related procedures, this solution is extremely time and manpower intensive when regarding a 
large number of systems and employment scenarios. 



- 25- 



Figure 2.3: 


Composite Utility Curve - Range at Maximum Speed 






7.5 


/ 






3 

< 
> 


/ 






£ 5.0 


/ 






-J 


/ 






2.5 


7 i i i i 


^j 




500 1000 1500 2000 


2500 




RANGE AT MAXIMUM SUSTAINED SPEED Irnnl 





Given the range of disparate opinion, the measurment validity of the composite curve is suspect. 
Perhaps more significantly, the wide range of responses reveals the daunting intellectual challenge con- 
fronting a panel of experts in determining precise value/utility matchups in a multi-faceted inquiry of this 

type. Each judge is required to make what amount to hundreds of discrete judgments which are consis- 

29 
tent within the variable being scored and across the family of variables. Individual judgments arc also 

predicated on the respondant's access to sufficient data concerning the variable and his interpretation of 

the scenario under which it is scored. Differing scenario interpretations probably contributed to much of 

the variance, even though Jacoby took great pains to detail the operating environment. The entire 

MAUT-based sea denial study constitutes a significant contribution to the field of military analysis and 

should be reviewed in toto by those considering application of the teclinique. However, for the purposes 

of this inquiry, it discussion will terminate here with the identification of those attributes relevant to the 

inquiry at hand. 



ttttttttttttttttttttt 
29 



As a rcspondant to two MALT surveys, the author has first-hand experience with the the difficulty 
of maintaining even meager consistency. The effort is so energy and time consuming that the poten- 
tial for obtaining a broad^sample of rigorously derived judgments is slim. 



- 26- 



2.4.3.3 Multi-Attribute Utility Technique Summary 

The most rewarding asset of applying MAUT to the analysis weapon systems' combat capability is that it 
incorporates informed expert judgment in all phases of the assessment process, an essential attribute of 
any reliable methodology. In particular, it offers an attractive solution to the combinational dilemma 
identified by LeGrow in aggregating individual factor scores. Additionally, it produces ratio level values 
measured from a common base which can be inserted in subsequent force level capability calculations. 
Conversely, MAUT suffers from a number of conceptual and structural liabilities. It does not legitimately 
scale nominally measured variables. Its implementation is cumbersome and prone to random judgmental 
influences which are well nigh impossible to isolate. Available methods for synthesizing disparate judg- 
ments are unsatisfying. While not a liability per se, MAUT's results are largely determined by the selec- 
tion of input variables and the validity of the data which describes them, a trait it shares with virtually 
every other approach. Multi-attribute utility technique resolves several of the more pronounced deficien- 
cies identified in other quantitative methodologies but introduces some of its own. 

2.4.4 TASCFORM Force Modernization Model 

The Analytic Sciences Corporation (TASC) developed a third quantitative methodology which incorpo- 
rates the performance characteristics of air weapons systems into combat relevant capabilities indices 
which can be evaluated on their own or aggregated into force level assessments. The air weapons assess- 
ment model, TASCFORM : -AIR, is a subset of a family of analytical models which address the subject 
of general purpose force modernization. The original models were developed in support of the Office of 
Net Assessment, Office of the Secretary of Defense, and have subsequently been applied to specific 
research questions in support of it and other government agencies. The TASCFORM methodology is 
not a statistical technique as such. However, it incorporates many of the same attributes addressed by the 
methodologies discussed above while maintaining the flexibility to consider meaningful attributes which 
are not amenable to interval or ratio level measurement. Consequently, its array of variables more com- 
prehensively defines the combat relevant attributes of an air weapon system than earlier efforts. It com- 
binational philosophy is predicated on mission specific expert judgment and can be expanded to account 
for the effect of difficult to quantify factors such as operator proficiency, maintenance and logistic support, 
and command, control, communications and intelligence (C I) support. 



ttttttttttttttttttttt 

30 

See, for instance, Congressional Budget Office, Tactical Combat Forces of the United States Air 

Force, pp. 31-50; and Assessment of Egyptian' Middle Fast Tactical Aviation Modernization (Classi- 
fied). A detailed description of the TASCFORM-AIR methodology is contained in Vogt, The 
TASCFORM Methodology: A 'Technique for Assessing Comparative Force Modernization, pp. 2-1 to 
2-55. TASCFORM is a trademark of The Analytic Sciences Corporation. 

- 27- 



2.4.4.1 Description 

The TASCFORM process follows a hierarchical path. A basic airframe system figure of merit is com- 
puted considering the values for four attributes (payload, range, maneuverability, speed) indexed to the 
value for a baseline system (the F-4B), weighted according to expert assigned values, and summed for 
each mission category. In all, three mission areas (air combat, surface attack, anti-submarine warfare) 
encompassing 13 distinct employment roles are evaluated for 112 fixed and rotary wing aircraft. Basic 
airframe scores are then modified through a series of calculations which account for the contribution of 
subsystems (target acquistion, navigation) and associated attributes (countermeasurcs susceptibility, sur- 
vivability) to mission performance. A final weapon system step adjusts performance indices to account 
for the systems' relative obsolence and sortie rate production potential. Finally, force level projections can 
be accomplished by allocating candidate inventories across mission areas and multiplying them by the 
corresponding performance indices. If desired, the resultant force level measures of merit can be further 
modified to account for the effect of intangibles such as C I, relative aircrew proficiency and the like in 
producing a final Equivalent Force Performance measure of merit. In all, TASCFORM-AIR represents a 
comprehensive, powerful, and operationally sensitive technique for quantitatively assessing the qualitative 
aspects of force modernization. While designed initially to address the US/Soviet force balance, it is 
equally applicable to assessments of the force structure and military balance aspects of arms transfer policy 
support. 

2.4.4.2 Application 

The full TASCFORM computational skein is too extensive to unravel in this overview. Just a few of its 
features will be highlighted to set the stage for further methodological development. As noted earlier, the 
initial calculation is anchored at the airframe level and considers payload, range, maneuverability, and 
useful air speed indexed to the corresponding value for the F-4B. A single variable is designated to repre- 
sent each attribute. For instance, maneuverability is pegged to the indexed value for specific excess power 
(P ). Herein lies the first deficiency in the approach. The selection of a single variable might well discard 
relevant information concerning an attribute which encompasses two or more dimensions. To use the 
manueverability example, P accounts only for energy maneuverability (acceleration), so the factor of lat- 
eral maneuverability (rate or radius of turn) is lost. Indexed values are modified by avionics and weapon 
system attributes to reflect their 'tactical impact' on basic airframe performance. The concept is solid, but 
execution is less precise than need be in two areas. Target acquisition capability is divided into four cat- 
egories (clear day, clear night, limited all-weather, good all-weather) winch are assigned subjective values 
(1.0, 1.0, 1.2, 2.0). This approach prohibits measurement of the very significant capability differences 
which obtain among target acquisition systems within these categories. For instance, the F-4E's 



- 28 



AN/APQ-120 radar and the AN/APG-70 being developed for the F-15E would receive equivalent scores; 
but there is no doubt that the actual performance capabilities of the two systems vary considerably. A 
similar situation prevails in the air-to-air missile category where differentiation is only made between gui- 
dance type and engagement mode (visual range or beyond visual range). Again, the combat relevant dif- 
ferences between missiles such as the all-aspect infra-red guided AIM-9L and the rear hemisphere only 
AA-8 are not captured. Similar observations could be made concerning the survivability and sortie rate 
attributes. 

2.4.4.3 TASCFORM Summary 

TASCFORM-AIR establishes an indisputably superior framework for the aggregation of combat relevant 
attributes into mission specific outputs. It incorporates expert judgment into a clearcut, flexible, and 
transparent combinational process and permits the consideration of important but intangible variables. As 
opposed to the other analytical models, it addresses the critical role target acquisition systems play in 
modern air warfare as well as permitting adjustments for employment related factors. On the debit side of 
the ledger, TASCFORM fails to make sufficiently granular assessments of the differences between specific 
subsystems in some cases. In the same vein, its reliance on single variables to describe primary system 
attributes sacrifices a measure of descriptive and operationally relevant information, perhaps unneccessari- 
ly. The negative aspect of this last feature might be partially offset by the implementational flexibility it 
offers. 

2.5 Methodologies Summary 

Regarding the sampling of military analysis methodologies which might be used to assist arms transfer 
decision making, it is obvious that the dollar valuation and inventory approaches are inadequate on their 
own to generate sufficiently informative assessments of the impact of an arms transfer on a nation's force 
posture in a vacuum or in a regional context. They simply do not measure or aggregate information reli- 
ably linked to combat capabilities. 

Factor analysis is capable of aggregating many of the essential elements but is volatile and unreliable 
when applied at the weapon system level. The forced inclusion of irrelevant data in producing specific 
attribute indices is factor analysis' greatest weakness, followed by its inability to process nominal data 
without output distortion. Additionally, a pure factor solution provides no operationally legitimate 
rationale for combining values for multiple attributes into a single system index, and the values themselves 
lack the ratio properties required for force level aggregation. 

Multi-attribute utility theory's greatest strength is its inclusion of expert judgment in all phases of the 
evaluation, providing a particularly effective scheme for combining values for multiple variables and attri- 



->( 



9 



butcs into a single measure of effectivess under a given scenario. However, it does not legitimately 
accommodate nominally described variables, and its administration is prohibitively cumbersome when 
applied to a subject with more than a handful of attributes and scenarios. 

The TASCFORM methodology is functionally comprehensive, situationally flexible, and operation- 
ally transparent and makes effective use of expert judgment. Variable input is unconstrained by measure- 
ment scales, and system output is well suited to modification and higher order aggregation. Its most pro- 
nounced drawback is a proclivity to over-simplify input data, masking significant performance differences 
within generic categories. 

In essence, no one methodology provides a holistic solution to the problem of incorporating qualita- 
tive information into quantitative military assessments. The common thread which connects them is a 
requirement for comprehensive mission relevant variable selection and thorough data collection and prep- 
aration. Since the application of any aggregation technique will succeed or fail on the basis of these fun- 
damental operations, variable selection and data collection will be addressed in the next two chapters. 
Subsequently, data reduction and aggregation techniques which capitalize on the strengths of the afore- 
mentioned models and minimize their weaknesses will be discussed. 



- 30 



Chapter 3 
VARIABLE SELECTION 

3.1 Structuring the Problem 

3.1.1 Defining Components 

Before individual measurement variables are considered, it is prudent to structure the research question 
more elaborately, identifying key components and their subcomponents. The importance of this step 
cannot be understated since even, "a highly sophisticated statistical analysis can rarely if ever compensate 
for a poorly conceived project or a poorly constructed data collection instrument.'' The problem at hand 
is to develop a measurement technique which assesses the impact of air weapons system acquisition on 
the air combat potential of Middle Eastern air forces. To structure or opcrationalize the problem, at least 
two major components must be meshed: 

• The performance potential of pertinent air weapon systems (aircraft plus specific subsystems) in 
definable employment categories (air weapon system combat potential). 

• The numbers of possessed air weapon systems a national air force could be reasonably expected to 
employ in identifiable classes of combat operations at given points in time (force propagation 
potential). 

A crucial challenge is the identification of attributes and supporting variables which most compre- 
hensively but efficiently capture essential combat related capabilities. The two main analytical branches 
described above must be supported by a network of functional subcomponents. In defining these second 
level focal points, an insensitivity to the texture of the subject and the operative rclationsliips between its 
parts can be debilitating. The omission of elemental attributes can undermine a model's relevance as was 
noted in the previous chapter. Consequently, variables must be selected with a keen eye toward the tech- 
nical complexities of the phenomena they seek to describe. As one research guide admonishes, 'good, 

2 
basic knowledge' of the subject area is a mandatory prerequisite. 



ttttttttttttttttttttt 

See Blalock, Social Statistics, p. 7. 

2 

Manheim and Rich, Empirical Political Analysis, p. 235. 

- 31 - 



3 J .2 Air Weapon Systems Subcomponents and Attributes 

With this injunction in mind, the air weapon system subcomponents displayed at the second level in Fig- 
ure 3. 1 are offered as an intermediate framework to guide the evolution of this inquiry. The listed sub- 
components are believed to define the predominant non-human elements which comprise an air weapon 
system. Looking to the left side of the second row in Figure 3.1, the first subcomponent is concerned 
with the combat potential inherent in the airframe itself. The term airframe will refer in this study to a 
basic aircraft, less avionics, target acquisition, and weapons systems. The next subcomponent addresses 
target acquisition and combat-significant avionics systems, while the third is comprised of aerial weapons. 
Defining the last two subcomponents distinct from the airframe provides an added bonus. Since few tar- 
get acquistion systems and even fewer weapons are airframe unique, their segregation at this juncture 
allows the construction of individuallly tailored air weapon systems configurations during the computation 
process. The function of the fourth subcomponent is not self-evident. With airframes and their subsys- 
tems treated separately, a mechanism is required to meld the potential represented by the subcomponents 
into a specific weapon system employed in a particular combat role. This relational task is the province 
of the last of the air weapon system's subcomponents. 

At the next rung down the analytical ladder, a basic step is the identifcation of those attributes which 
define the relative performance potential of a weapon system subcomponent. Several air combat oriented 
publications and studies suggest a variety of candidates. The most operationally relevant of these were 
flagged as key subcomponent performance attributes. 

Airframe. A USAF Tactical Air Command Fighter Weapons' School manual pinpointed two 
attributes essential to airframe performance: speed and maneuverability. Gunston and Spick's 
Modern Air Combat suggested a third: combat persistence or endurance. The fourth, vulnerability 
to engagement, was derived from discrete concepts found within these two documents and the 
TASC study. 5 

Target Acquisition and Avionics Systems. Isolating attributes for this subcomponent is made some- 
what nebulous by the variety and different purposes of the systems involved. However, two generic 
attributes appear common: the performance capacity of the system measured on whatever scale is 

germane and the system's vulnerability to degradation or incapacitation. 

ttttftttttttttttttttt 

This structure draws heavily on ideas outlined in The Analytic Sciences Corporation s 
TASCFORM-AIR model and on notes pertaining to the calculation of'measures of air combat merit 
prepared by operations analysts at Northrop Corporation's Aircraft Division. 

For the purposes of this study, avionics will be limited to navigation systems, fire control computers, 
and head-up displays. The aerial weapons category includes guns, air-to-air missiles and air-to-ground 
ordnance. 

See USAF Fighter Weapons School, Basic Aerodynamics, pp. 3-20 to 3-22; Gunston and Spick. Mod- 
ern Air Combat, pp. 186- 193; and The Analvtic Sciences Corporation, The TASCFORM Methodology, 
pp.2- 14 to 2-15. 

- 32- 



FORCE POTENTIAL 



I 






Air Weapon System 



i 



T^l 

! Propagation ' 
i J 



I Airframe I I Target Acquisition I I Payload I I Relational! 



-TJj 



eed 



] 



■I Performance _| Lethality I J Configuration! 



■4 Maneuverability I I— I Vulnerability I LI Effectiveness |L| Utility 
— I Endurance 
-4 Vulnerability 



Figure 3.1: An Analytical Typology: Air Weapon System Component 



Aerial Weapons. Again, the disparate natures of the systems results in the designation of generic 
attributes which are a bit vague but which capture the essential combat qualities of a weapon: its 
lethality and its effectiveness in overcoming countermeasures. 

Relational Factors. This subcomponent encompasses two attributes. First, subsystems need to be 
related in time and space. Second, they must be related in terms of their proportional contribution 
to mission output. These two attributes are referred to as configuration and relative utility respec- 
tively. 

3.1.3 Force Propagation Subcomponents and Attributes 

The assembly of a family of attributes which credibly define the boundaries to realization of combat 
potential for each nation over time is a daunting task. Authoritative military and academic literature 
leaves no doubt that a nation's ability to support and operate combat weapons systems is a critical deter- 



33 



minant of military effectiveness. Former Israeli Air Force Chief of Staff Ezer Weizman emphatically 
stated that these largely human factors, '. . .will decide the fate of war, of all wars. Not the Mirage or any 
other plane. . .' While this point might be somewhat overstated, there is no arguing with its essence. 
Unfortunately, the individual and national variables which define such attributes as leadership, technical 
acuity, planning insight, and operator proficiency are virtually impervious to operationalization in the 
aggregate. Heroic attempts have been made to isolate the variables associated with national support 
potential and operator proficiency. However, a thorough review of the suggested methodologies sub- 
stantiated that they involved collection of information concerning variables which would greatly exceed 
the resources of this research effort (e.g., aircrew training and continuation flying hours) or surrogate vari- 
ables whose relationships to the attributes they were stipulated to represent were tenuous. 

As an additional consideration, the measurement techniques suggested by most researchers who have 
attacked this problem focus on those variables which might conceivably capture some portion of a 
nation's 'microcompetance' to operate and employ weapons systems. No systematic measure of the 
equally important attribute of the 'macrocompetance' required to organize and employ the weapons is 
available. A review of three decades of Israeli air victories in the Middle East suggests that the latter is 
just as important as the former. For these reasons, the effort to derive national measures of merit for 
operator proficiency or employment effectiveness was deferred to other researchers. Indeed, it is probable 
that regional experts can subjectively factor in these considerations with greater validity and efficiency than 
can be generated by a fixed computational scheme. 

As a result of this determination, the evaluation of employment factors in this study is limited to 
those factors which inscribe an outer boundary on a nation's capability to generate its combat forces. 
With this caveat, the analytical typology dealing with force propagation is displayed in Figure 3.2. Obvi- 
ously, the inventory of air weapon systems possessed by a force is a necessary point of departure. This 
gross total must be further elaborated by a term which reflects their likely allocation to given combat 

roles. To complete the picture, some measure of a nation's cumulative potential to emplov the opera- 

tttttttttttttTttttttt 

Excellent discussions of realistic constraints imposed by operational and support capabilities can be 
found in Pascal et al, Men and Arms in the Middle East; de Leon, The Peacetime Evalutalion of the 
Pilot Skill Factor in Air- to- Air Combat; Kemp, Arms and Security; and DuPuy, 'Measuring Combat 
Effectiveness'; among others. 

Quoted in Lambeth, Moscow's Lessons from the 1982 Lebanon Air War, p.31. Ironically, Israel has 
consistently pressed for the subsidized acquisition of the most advanced American systems and ener- 
getically contested the Arab acquisition of the same or lesser capabilities. 



8 
9 



See, for instance, Benjamin Lambeth's comments in 'Pitfalls in Fighter Force Planning', p. 16. 

See in particular Pascal et al. op.cit., Timperlake and Lcvecn, A Methodology for Estimating Compara- 
tive Aircrew Proficiency, and Leveen and Vogt, A Methodology for Assessing Grc 



Uroundcrew Proficiency 



This is an adaptation of the injunction credited to Alain Einthoven, 'The point is to render unto 
computers the things that are computers and to judgment the things that are judgments.' Quoted in 
USGAO, Models, Data, and War, p.73. 

-34- 



tionally available inventory in the combat roles to which they have been allocated must be derived. The 
ability to generate assets is the product of three attributes: the proportion of the force available for combat 
operations, the maintenance support they require, and the maintenance resources on-hand to service 
them. 1 1 



FORCE POTENTIAL 



Force 



A Inventory 
—4 Allocation I 






. 1 - 1 

I Air Weapon System j I Propagation I 



[ 



Generati 



on I 



— | Availability 

■i Mx Requirement 
— I Mx Availability 



Figure 3.2: An Analytical Typology: Force Propagation Component 



Regarding Figure 3.1 and Figure 3.2 together, the attributes identified at the third level of the hier- 
archical structure represent the basic blocks with which a force level combat assessment can be built. As 
such, they constitute a map to guide the search for potential capability measurement variables. The 
numbers of variables describing a particular attribute might be as few as one or as many as ten or more. 
Their selection is a function of the nature of the attribute, the relevant observations which pertain to it, 
and the availability of descriptive data. 



ttttttttttttttttttttt 

The abbreviation 'Mx' is used as a shorthand term to describe maintenance. 

- 35- 



3.2 Variable Selection Guidelines 

Even within these structured confines, the plethora of candidate variables far outstrips processing or intel- 
lectual resources. Consequently, the explanatory power of possibly pertinent variables has to be screened 

finely to extract the minimum number which explain the maximum significant variance in air weapon 

12 
system and national performance potential. The number of variables linked to an attribute should not 

be so harshly pruned that comprehensive evaluation becomes illusory. On the other hand, redundant 
variables which capture the same essential facet of an attribute need to be eliminated to avoid analytical 
distortion. The more definitive the scale on which a variable is measured, the more precise are the results 
which can be obtained from its analysis. Consequently, ratio or interval scaled variables are preferrable to 
those valued on nominal or ordinal scales. However, ratio or interval level measures are not always 
applicable to or available for key variables. While nominally described variables are not fully amenable to 
some statistical processes, they should be included in the analysis if no legitimate alternative exists. Cap- 
turing the effect of relevant attributes is more critical than adulterating the substance of the problem to 
accommodate sophisticated statistical techniques. 

A final temptation to be eschewed is the substitution of accessible 'surrogates' for qualities which are 
not directly observable or or easily quantifiable. The use of surrogates is not in itself an unsound practice: 
but each surrogate must be subjected to rigorous scrutiny before inclusion. The incorporation of surro- 
gate variables which are only minimally or coincidentally related to the qualities they are designated to 
represent cannot help but distort the resulting analysis from a substantive standpoint, often lethally. 

In the same vein, the creation of composite or index variables stipulated to stand in for a more com- 
plex and mathmatically indescribable characteristic must be treated cautiously. Indices frequently convey 
meaningful performance related information unobtainable through any single component measure. In the 
realm of aircraft, thrust-to-weight ratio, wing loading, and wing aspect ratio are all widely recognized as 
legitimate indicators of energy maneuverability, turning capability, and relative lift respectively. However, 
indices are legitimate only when their components have a functional impact on the characteristic being 
represented and their combinational mode reflects an engineering or operational reality. A poorly chosen 
surrogate or an invalidly constructed composite variable not only can miss the mark, it can lead the anal- 
ysis astray. 

In consonance with the preceding, some basic ground rules are offerred to govern the identification of 
study variables. 



ttttttttttttttttttttt 

12 

This principle is often referred to as 'parsimony' and is commonly acclaimed as one of the key attri- 
butes of any higher-order research effort. See, for instance, Manheim and Rich, op.cit., p. 353. 

- 36- 



• A list of candidate variable supporting the analytical structure described above should provide 
broadest practicable explanation of the sources of variance implicit to each attribute. 

• Variable lists should be culled to the rninirnum required to explain combat relevant variance, elimi- 
nating redundant measures. 

• Comprehensive attribute representation should overrule concerns for parsimony. 

• Variables should be selected which represent the highest level measurement of the attribute being 
portrayed but should not be eliminated if only measurable at a lower level. 

• Surrogate variables should be used only as a last resort, and composite variables only when func- 
tional or operational precedents had been established. 

3.2.1 Variable Selection Process 

3.2.1.1 Air Weapon Systems 

A list of candidate system variables was compiled in 'shopping list' fashion, relying on attributes fea- 
tured in publications such as Jane's All the World's Aircraft, USAF Fighter Weapons School's Basic Aer- 
odynamics, and Modern Air Combat. Other variables were gleaned from periodicals such as Aviation 

Week and Space Technology and Air Force Magazine. Finally, variables considered in other military 

13 
analyses were appended to the list if not previously included. As a final test of inclusiveness, the vari- 
able list was submitted to a panel of three fighter pilots and one intelligence expert for review, and their 
revisions incorporated. 

The initial 300 variable list was exhaustive but unweildly and inappropriate for further action without 
aggressive winnowing. It is immediately evident that collecting data on this number of variables is over- 
whelming, even in the unlikely circumstance that the requisite data were available in unclassified sources. 
Some categories of of variables had to be simplified to permit concentration on the most salient combat 
related attributes. Avionics systems with important combat performance implications are treated generi- 
cally as nominally scored single variables. For instance, the variable 'NAVCAT' cites navigation system 
type, and the presence of head-up displays and integrated fire control systems is captured in nominal vari- 
ables. The profusion of air to ground weapons systems and the multiplicity of associated characteristics 

1 A 

make them a particularly unweildly variable group. Nonetheless, categorical variables are retained to 
indicate an aircraft's precision guided munitions capability and type, partially accounting for advanced 
weapons capacity. Finally, the question of assessing air weapon ground support requirements tlirough 

13 • • 

For instance, LeGrow offers a thorough discussion of some performance variables and the dimen- 
sions they capture, while TASCFORM's charts and equations irive a good overview of the attributes 
and their inter-relationships. See also Cordesman, Jordanian Arms and The Gulf and the Search for 
Security 

It is reassuring to note that The Analytic Science Corporation arrived at the same conclusion con- 
cerning air to ground weapons in their quite exhaustive study. 

- 37- 



14 



analysis of a family of maintenance variables was deferred. Instead, the single variable, Man- Maintenance 
Hours Per F ; lying Hour (MMII/FH), recommended by Epstein as the best single indicator of support 



complexity, was introduced 



15 



3.2.1.2 Airframes 

Application of the above considerations reduces the number of variables to be considered to man- 
ageable proportions. In addition, the structure was modified slightly to facilitate automated manipulation 
and statistical processing. The initial complement of variables intended to portray the attributes of an 
airframe itself is displayed in Table 3.1 The variables annotated with asterisks (*) are measured on a nom- 
inal scale. Definitions of the variables follow the table. A complete file description is in Appendix A. 



Table 3.1: Airframe Variables 



Aircraft 
Wing Span 
Wing Aspect Ratio 
Empty Weight 
Combat Wing Loading 
Fuel Fraction 
Maximum Thrust 
Variable Wing" 
Maximum Airspeed 
Maximum Airspeed 
Rate of Climb SL 
Rate of Turn 
Service Ceiling 
Attack Radius 



Maximum Ordnance 
Internal Guns 



Role 

Wing Surface 
Combat Weight 
Maximum Weight 
Internal Fuel 
Combat G Limit 
Thrust-to-Weight 
Variable Camber- 
FL360 Specific Energy At 
SL Specific Energy SL 
Stall Speed 
Specific Excess Power 
Intercept Radius 
Combat Range 
Weapons Stations 
Gun Rounds 



Ratio 

Altitude 



Aircraft. 
Role. 



Wing Span. 



The name and variant of the aircraft. 

Defines the aircraft system type (e.g., fighter-interceptor, bomber-ground attack). Not to 

be confused with nationally determined employment codes which are associated with the 

inventory subcomponent. 

Distance from wing-tip to wing-tip, not considering tip mounted stores. 



ttttttttttttttttttttt 
15 



16 



Epstein, Measuring Military Power, p. 19. 

The Statistical Package for the Social Sciences, release Ten (SPSSX) was used for the creation of 
data files and all statistical and computational processing. A micro-computer based set of files and 
procedures is currently under development. 



38 



Wing Surface. 

Total wing surface area, not considering tip mounted stores. 

Wing Aspect Ratio. 

Describes the planform shape of a wing, a factor which affects the wing's lift coefficient. 

Combat Weight. 

A weight calculation which defines the likely gross weight of an aircraft when engaged in 

combat (as opposed to maximum takeoff weight). 

Empty Weight. 

The weight of an aircraft fully equipped less fuel and stores. 

Maximum Weight. 

The maximum takeoff weight of an aircraft fully fueled and loaded with stores. 

Internal Fuel. The internal fuel capacity of an aircraft measured by weight. 

Wing Loading. 

The ratio of combat gross weight to wing surface area. Indicates the relative turning per- 
formance of an aircraft, with an inverse relationship between the two. 

Fuel Fraction. 

Compares the internal fuel weight of an aircraft to its combat gross weight as an indicator 

of combat persistence. 

Combat G Limit. 

The maximum centrifugal force, expressed in terms of acceleration of gravity, an aircraft is 

designed to withstand in maneuvering combat. 

Maximum Thrust. 

The maximum 'wet' (with afterburner) thrust which an aircraft's powerplant can generate 

at sea level. 

Variable Wing. 

Notes the presence of a variable geometry or 'swing' wing. 

Variable Camber. 

Notes the presence of devices such as leading edge slats or maneuvering flaps which 

change the camber of wings in flight, thereby improving turning performance. 

Thrust-to- Weight Ratio 

Compares the combat gross weight of an aircraft to its installed thrust as an indicator of 

its ability to accelerate and sustain turn rates. 

Maximum Airspeed FL360. 

Measures maximum airspeed in a high altitude profile. This altitude (36,000 feet) was 

selected as it represents the high end of a likely combat envelope under most scenarios. 

Maximum Airspeed SL. 

Measures maximum airspeed at sea level. Sea level was selected as representative of the 

low end of the combat envelope, at which aircraft might well have significantly different 
speed capability than at higher altitudes, thus giving a better perspective of useful speed'. 



39 



Specific Energy Alt. 

A measurement of the total mechanical energy (kinetic plus potential) of an aircraft at its 

maximum air speed and service ceiling. 

Specific Energy SE. 

As above, except measured at sea level. 

Stall Speed. Speed at which the aircraft's drag exceeds its aerodynamic lift in level flight. 
Rate of Turn. The maximum instantaneous level turn performance an aircraft can achieve at sea level in 
clean configuration. 

Specific Excess Power. 

Measures an aircraft's ability to change its energy state by accelerating. Calculated at a 

particular condition of flight (10,000 ft, Mach .9, level flight in this instance). 

Service Ceiling. 

Altitude above which aircraft is incapable of further acceleration. 

Intercept Radius. 

Maximum radius at which a normally air-to-air mission configured aircraft can conduct a 

sub-sonic area intercept mission. 

Attack Radius. 

Maximum radius at which a normally air-to-ground mission configured aircraft flying a 

hi-lo-lo-hi profile can attack a target. 

Combat Range. 

Maximum range at which an aircraft can conduct its primary combat mission. 

Maximum Ordnance. 

Maximum weight of air-to-ground ordnance which the aircraft can carry. 

Weapons Stations. 

Number of weapons stations available for air-to-ground ordnance. 

Internal Guns. 

Number of guns mounted internally to the aircraft. 

Gun Rounds. Number of rounds of ammunition normally carried for the internal gun(s). 

3.2.1.3 Target Acquisition Systems 

The next data set is comprised of performance variables associated with target acquisition attributes. 

While it consists of variables measured on both ratio and nominal scales, only the ratio level variables are 

candidates for statistical manipulation. It is displayed in Table 3.2, with nominally measured variables 

annotated (*). 

Name. Most frequently, the alpha-numeric designator assigned to the system. In the case of U.S. 

systems, the leading 'AN' portion of the designator has been dropped. For those systems 
for which the designator is not published in open sources, such as the SU-27 Flanker, a 
descriptive entry (i.e., 'FLANRAD') is used. 



-40 



Table 3.2: Target Acquisition System Variables 

Name Code 

Output Power Coverage 

Range-High Target Range-Low Target 

Data Points , Track While Scan* 

CW Illumination-'' ( Ground Mapping" 
Doppler Beam Sharpening* ECCM Capability"" 



Code. A four letter descriptor of system type. The first two letters describe the system's generic 

category (e.g., 'RA' for radar, 'LA' for laser) and the second two address its primary 
employment role (e.g., 'AI' for air-intercept, 'GA' for ground-attack). 

Output Power. 

Actual or equivalent power emitted by system. 

Coverage. Angular lateral coverage provided by the system, akin to the field of view. 

Range- High Target. 

Maximum range at which a fighter-sized target operating at the same or higher altitude 

could be detected. 

Range-Low Target. 

Maximum range at which a fighter sized target operating at lower altitude could be 

detected. 
Data Points. The number of relevant information points (such as range, bearing, altitude, airspeed) the 
system generates concerning the target. 

Track While Scan. 

Ability to continue to scan for potential threats while tracking the highest tlireat target(s). 

CW Illumination. 

Ability to provide the continuous wave target illumination required to guide semi-active 

radar homing air to air missiles. 

Ground Mapping. 

Ability to provide radar display of ground environment with sufficient resolution to iden- 
tify geographic or cultural features. 

Doppler Beam Sharpening. 

Ability to increase resolution of ground map display so that targets or wayppoints can be 

easily identified. 

ECCM Capabilitv. 

Indicator of system's relative resistance to electronic counter measures through features 

such as side-lobe suppression or frequency agility. 



41 



3.2.1.4 Air-to-Air Missiles 

The next variable set, outlined in Table 3.3, is comprised of variables associated with air-to-air mis- 
siles. As with target acquisition systems, this table lists variables measured on both ratio and nominal 
scales. Nominally scaled variables are not being considered for statistical processing, although they will 
eventually be involved in combat potential computations. 



Table 3.3: Air to Air Missile Variables 

Missile Diameter Missile Length 

Missile Weight Terminal Guidance Mode 

Maximum Range-Head On Minimum Range-Head On 

Effective Range-Head On Maximum Range-Tail 

Minimum Range-Tail Effective Range-Tail 

Warhead Weight Fuzing Options 

Maximum Speed t G Limit 

ECM Susceptibility* Guidance Score" 
Acquisition Mode" 



.•- 



Missile Diameter. 

Diameter of missile's body. 

Missile Lengtia. 

Length of missile. 

Missile Weight. 

Gross weight of the missile. 

Terminal Guidance Mode. 

Method (semi active radar homing, infrared, active radar homing, command guided, etc.) 

by which missile is guided during its terminal phase. 

Maximum Range- Head On. 

Maximum range against a target which is converging with the launch platform from the 

forward hemisphere. 

Minimum Range-Head On. 

Range from the launch platform within which the missile is ineffective against a target 

approaching from the forward hemisphere. 

Effective Range-Head On. 

Range envelope within which the missile is effective against a target approaching from the 

forward hemisphere. 

Maximum Range-Tail. 

Maximum range against a receding target. 

Minimum Range-Tail. 

Range from the launch platform within which the missile is ineffective against a receding 

target. 



-42 



Effective Range-Tail. 

Range envelope within which the missile is effective against a receding target. 

Warhead Weight. 

Weight of missile warhead. 

Fuzing Options. 

The number of fuzing methods available. 

Maximum Speed. 

Maximum missile speed to burnout. 

G Limit. The maximum centrifugal force, expressed in terms gravitational acceleration, the missile 

can accept; an indicator of maneuverability. 

ECM Susceptibility. 

A relative measure of the missile guidance system's susceptibility to defeat by electronic 

combat measures such as flares, chaff, or jamming. 

Guidance Score. 

An indicator of relative guidance system accurancy. 

Acquisition Mode. 

Indicates if guidance system is capable of locking-on to a target beyond visual range. 

3.2.1.5 Aerial Guns 

The final weapon system table, Table 3.4, lists key variables associated with aerial gun systems. All 
of the variables are measured at the ratio level. 



Table 3.4: Aerial Gun Variables 

Calibre Maximum Effective Range 

Dispersion Muzzle Velocity- 

Rate of Fire 



Calibre. Calibre of gun 

Maximum Effective Range. 

Maximum range at which projectile maintains sufficient velocity to remain effective. 

Dispersion. A measure of relative accuracy which reflects dispersion of rounds around a mean point 

of impact. 

Muzzle Velocity. 

Projectile velocity as it exits the gun. 

Rate of Fire. Maximum number of rounds which the gun can fire in a minute. 



-43- 



3.2.1.6 Relational Variables 

Aircraft Configuration. This set of variables mates the airframe with its subsystems (target acquisition and 
weapons). In addition, it contains those combat-related performance variables which are not suited to 
statistical manipulation but which still need to be considered in calculating air combat potential. For ease 
of manipulation, these are assembled in the configuration file shown in Table 3.5. As was the case previ- 
ously, variables are defined following the table. Variables involved in mission potential computations are 
annotated (*), and a formal file description is located in Appendix A. 



Table 3.5: Aicraft Configuration Variables 

Crew Members* f Air Refueling Capable", 
Navigation Category* Radar Warning Receiver" 
Passive ECM* Active ECM* 
Radar System , Other Target Acquisition 
Head Up Display" Stability Augmentation- 
Radar Guided AAM Number Radar AAM* 
Infrared Guided AAM Number Infrared AAM- 
Gun System , PGM Capable- 
Release Point Computer*" Maintenance Hours Per Flying Hour- 
Production Country 



Crew Members. 

Number of aircrew members normally assigned. 

Air Refueling Capable. 

Indicates if aircraft is capable of aerial refueling. 

Navigation Category. 

Identifies most sophisticated category of navigation system fitted to the aircraft. 

Radar Warning Receiver. 

Indicates presence of an electronic warfare threat receiver (detector). 

Passive ECM. 

Indicates capability to dispense non-intrusive electronic combat expendables such as flares 

or chaff. 
Active ECM. Indicates equippage with internal or external radar jamming or deception systems. 

Radar System. 

Identifies the target acquisition radar (air intercept, air-to-ground, or multi-mode) installed 

in the aircraft. 

Other Target Acquisition. 

Identifies additional target acquisition systems (infra-red search track, laser, forward- 
looking infrared) installed in or on the aircraft. 



44- 



Head Up Display. 

Identifies the presence of a system which displays operational and combat related data on 

a combining glass at eye level. 

Stability Augmentation. 

Measures to increase platform stability during air-to-ground weapon delivery. 

Radar Guided A AM. 

Identifies the radar guided air-to-air missile normally carried on the aircraft. 

Number Radar AAM. 

The number of radar guided AAMs normally carried. 

Infrared Guided AAM. 

Identifies the infrared guided AAM normally carried by the aircraft. 

Number Infrared AAM. 

The number of infrared AAMs normally carried by the aircraft. 

Gun System. Identifies the aerial gun normally mounted internally. 

PGM Capable. 

Indicates aircraft potential to deliver precision guided air-to-ground munitions. 

Release Point Computer. 

Indicates presence of a computer which provides a CCIP/CCRP type solution for release 

of bombs. 

Production Country. 

A code which describes the initial country of production for the air weapon system. The 

singular exception are a few indicators which credit a host country such as Israel with 
making such drastic modifications to the aircraft that it is drastically different from its 
antecedant. 

Maintenance Hours Per Flying Hour. 

An estimate of the man-maintenance hours required to support one flying hour by a par- 
ticular system. 
Relative Utility. The problem of identifying variables which relate system and subsystem attributes to 
mission output potential presents a thorny challenge. No definitive methodology entirely congruous with 
the objectives of this project could be identified, although the TASCFORM model embodies many 
applicable concepts. Applying TASC's concepts in conjunction with advice from air operations experts, 
those junctures were isolated at which key combat related attributes were joined, building from the sub- 
component to the full air weapon system level. For example, if an airframe possesses attributes categor- 
ized as speed, maneuverability, and endurance, these would interact in varying proportions to contribute 
to combat success in particular missions. At a higher level, the summed attributes of the airframe would 
interact with the summed attributes of the the target acquisition system and pay load in proportions the 
values of which would be differentiated by mission. Employing this 'building block' approach, the list of 
variables shown in Table 3.6 designates the juncture points. The values for each variable represent the 



45 



relative utility of a given attribute at a given juncture. To eliminate redundancy, each entry actually rep- 
resents four variables, one for each of the projected combat roles: air defense, air superiority, interdiction, 

17- 
and close air support. The breaks in the table represent the progression of 'blocks' building to full air 

18 
weapon system potential. 



Table 3.6: Relative Utility Value Variables 

Airframe Component 
Airspeed Utility Maneuverability Utility 

Combat Endurance Utility 

Payload Component 
Infrared AAM Utility Radar AAM Utility 
Gun Utility Unguided Ordnance Utility 

Guided Ordnance Utility 

Target Acquisition Component 
Visual System Utility Radar System Utility 
Secondary System Utility 

Engagement Vulnerability Component 
Airspeed Utility Maneuverability Utility 

ECM Utility Signature Utility 

Air Weapon System 
Airframe Utility Acquisition System Utility 

Payload Utility 



3.2.2 Force Propagation Variables 

Two alternative variable definition strategies were considered for assembling inventory data. Much of the 
arms transfer literature concentrates on describing and evaluating the flow of weapons and associated 
capabilities. While this approach has its merits, evaluating the combat potential which results from the 
transfers involves the broader task of fixing those capabilities in the context of a national and regional 
force structure. Additionally, the task of assembling a unfied body of reliable data on the flow of arms is 
fraught with uncertainty. The potential for gleaning accurate data on major systems once they have been 
introduced into an inventory is more promising than attempting to capture them in the pipeline'. 



ttttttttttttttttttttt 

17 - 

The formal description for this file is not presented in Appendix A, since the file is actually composed 

of 76 discrete variables cryptically identified. The presentation in Table 3.6 should convey sulticent 



18 



information to grasp its content adequately. 

The 'Vulnerability Component' constitutes a factor which depreciates the combat potential of the 
entire air weapon system. As such, the relative values for its subcomponents need to be identified, 
but it has by definition a relative utility of unity. 

-46- 



3.2.2.1 Inventory 

Consequently, an inventory approach was selected. To preserve the capability to track combat potential 
back to the arms transfer source, the country of production variable in the system data sets could be 
employed. An additional consideration is the identification of the likely employment of a weapons system 
by a given country. Consequently, a variable stipulating employment code is necessary. Table 3.7 lists 
the inventory related variables on which data would be collected. While the formal inventory file, 
described in Appendix. A, includes information at the weapon system level only, a separate listing of sub- 
systems available to a given country was prepared off-line for entry as variable values in the system con- 
figuration file. 



Table 3.7: Inventory Variables 

Country Code 

Weapon System Name 

Employment Code 

Weapon System Inventory 

Operational Availability Rate 



Country Code. 

A two letter code, corresponding to DoD standard usage, which identifies the country 

possessing the weapon system. 

Weapon System Name. 

The name of the air weapon system. Identical to aircraft name. 

Employment Code. 

An alpha-numeric code which identifies the likely combat role of the unit to which an air 

weapon system is assigned (e.g., 'FGA' for fighter-ground attack, 'FMR' for fighter-multi- 
role). 

Weapon System Inventory. 

The number of a particular aircraft possessed by a country in a given year. 

Operationally Available Rate. 

The estimated fraction of possessed aircraft which would be available for operational 

employment. 
3.2.2.2 Employment 

As noted earlier, this study will limit its employment purview to those quantifiable attributes which imp- 
inge directly on a national air force's capability to generate a multiple (sortie rate) of the combat potential 
embodied in its individual weapon systems. Joshua Epstein convincingly demonstrated the viability ol' 



-47 



this concept in evaluating the Soviet air threat to Europe. Epstein contends that by weighing the amount 

of maintenance required by an inventory of aircraft against the amount of maintenance available, the ana- 

19 
lyst can set a sortie generation boundary. While Epstein acknowledges the important roles personnel 

quality, doctrine, and organization play in determining actual rates within an outer sortie generation 
boundary, he asserts that calculating the boundary at least defines the 'worst case' even when all the other 
variables are assumed to be equal. Operationalizing the problem requires that the researcher collect data 
which describes the maintenance requirement imposed by each aircraft, the maintenance resources avail- 
able to the national air force, and the employment scenarios in which the force will be employed. 

To inject a greater differentiation and realism into the problem, additional qualitative variables will be 
considered on an experimental basis. One study by The Analytic Sciences Corporation concluded that the 
quality of ground support is the product of the motivation and technical acuity of the servicing ground- 
crews. The technical acuity dimension is measured by assessing relative educational levels and the effects 
of exposure to technical systems like automobiles and telephones. These measurements are modified by a 
term which estimates the range of the population to which the average technical value would apply and 
accounts for the influence of foreign advisors. Motivation is purportedly captured by scaling nations on a 

psychologically oriented matrix which assesses relative adherence to the 'active mastery' theme inherent in 

21 
the 'Protestant Ethic'. While this approach might well be valid, the underlying psychological principles 

and assignment criteria are too speculative to be applied here. Consequently, variables suggesting motiva- 

22 
tion were drawn from two other studies which addressed an analogous subject. These include the 

number of armed forces per thousand, military expenditures per capita, and military expenditures per 

GNP. The latter two variables also provide some indication of the relative investment in support 

resources being made by the country concerned. The resulting employment variable set is depicted in 

Table 3.8. Only the top two quantitative variables will be included in the baseline methodology. The 

remainding qualitative variables will be employed for experimental purposes only and are by no means 

definitive. 

ttttttttttttttttttttt 

19 

Another study focused on Europe contends that, in the European environment at least, the availabil- 
ity of pilots might be an even more potent predictor of sortie generation boundaries. See Alberts. 
Deterrence in the 1980' s: Part II, The Rule of Conventional Air Power, p. 32. Tliis limitation will 
apply even more stringently in most Third World countries. Unfortunately,^ its consideration was 
deferred because of the predictable lack of aircrew information at the unclassified level. However, it 
is a factor which might be reintroduced if sufficient information became available. 



20 



21 
22 



Discussions with Northrop Corporation analysts revealed that thev include estimates of sortie dura- 
tion by mission type, the length of the flying day, and the length of the maintenance day in their sor- 
tie generation computation. While the methodology they employ is considerably more sophisticated 
than the one contemplated here and is anchored at the weapon system rather than force level, their 
approach is generally consistent with Epstein's. 

See Leveen and Vogt, A Methodology for Assessing Groundcrew Proficiency, pp. 2-1 to 2-34. 

See Timperlake and Leveen, A Methodology for Estimating Comparative Aircrew Proficiency, p. 3- 11; 
and Pascal et al, op.cit., p. 38. 

-48- 



Table 3.8: Sortie Generation Variables 

Maintenance Hours Required 

Maintenance Hours Available 

Literacy Rate 

Percentage Eligible in Secondary School 

Armed Forces Per Thousand 

Military Expenditures Per Capita 

Military Expenditures Per GNP 

Military Expenditures Per Government Expenditures 



3.3 Summary 

This section has outlined a methodological structure which will be employed to channel the collection of 
data relevant to the assessment of the combat potential of Middle Eastern air forces as a function of their 
acquisition of air weapon systems. The overall problem was decomposed into two components: one 
which addresses the combat potential inherent in the systems themselves and a second which considers the 
force propagation potential of the operating nation. Each component is further segmented into a hier- 
archy of subcomponents, attributes, and the variables which describe them. The structure created in this 
chapter in essence constitutes a data collection plan, the implementation of which will be discussed in 
Chapter 4. 



49 



Chapter 4 
DATA COLLECTION 

4.1 Collection Boundaries. 

Since the goal of this study is to evolve a workable methodology rather than to provide universally appli- 
cable substantive solutions, it was necessary from the outset to draw some boundaries for data collection 
and analytical focus. The regional boundary (Middle East) has already been drawn, but some additional 
limitations need to be imposed. Though the definition of these boundaries restricts the playing field 
somewhat, the essentials of the game are preserved. 

4.1.1 Temporal. 

Only those combat aircraft employed in the region during the last decade or which might reasonably be 
introduced into it during the next will be considered. This temporal limit might appear to conflict with 
the injunction laid down by other researchers to construct evaluation schemes valid over time, with data 
bases looking back to World War II vintage aircraft. Historical merit aside, such a broad approach seems 
unduly effusive in a scheme geared primarily to forward looking evaluation. 

4.1.2 Functional. 

A further limitation is to concentrate on those aircraft involved in primary combat roles. Consequently, 
systems such as the E3A/AWACS, E2C/lIawkeye, reconnaissance platforms, and airborne tankers are not 
included, although they support combat operations. Similarly, aircraft whose sole function is aircrew pri- 
mary training are not included, but those advanced or conversion trainers which could be easily shifted to 
a combat role are. Einally, rotary-wing combatants are not addressed in this initial study, although they 
promise to play an increasingly significant role in Mideast combat. These restrictions on systems consid- 
eration limit the field somewhat severely and regrettably exclude some important support aspects of com- 
bat potential estimation. Nonetheless, the inclusion of over 120 combat aircraft makes it a representative 
and viable data set. 



ttttttttttttttttttttt 

Some reconnaissance and training versions of combatant aircraft were included in the initial data h.-i^e 
compilation and analysis phases and are displayed in the orders of battle. However, no combat poten- 
tial scores were computed for them. 

- 50- 



4. 1 .3 Informational. 

A final note on limitations is intended primarily for U.S. Government users. The data included in the 
various study data bases are taken strictly from open source, unclassified materials. As a result, individual 
data values might be at odds with those reflected in classified documents. Additionally, the author was, at 
times, required to rely on an estimative process to arrive at data values he recognizes are specified more 
precisely in authoritative classified data bases. This limitation was imposed for two reasons. Large-scale 
automated statistical processing could not be conducted in a classified environment at the research institu- 
tion. Also, a classified product would not be widely available for the critical review and comment of aca- 
demic researchers. The unclassified data, although less precise, satisfactorily describe key variances, and 
the penalty paid in accuracy by using them will be outweighed by the value of critical comments from the 
academic community. 

4.2 Some Collection Principles. 

4.2.1 Leveling the Field. 

The research and intelligence communities are often captivated by the illusion that there is somewhere a 
number which reflects 'truth' with a capital 'T'. In reviewing the many publications and articles offering 
information on weapon system characteristics and inventories, one is struck by a multiplicity of contend- 
ing 'truths'. There is a profusion of data on many variables, but a substantial portion is contradictory and 
of undefined derivation. The producer claims the ground attack radius of an F-20A is 550NM, while 
other sources list it as 455NM and 595NM respectively. One very well informed author alternatively 
notes the AN/APG-66A (now termed AN/APG-68) radar has a maximum target acquisition range against 
a low altitude target of 47NM in one book and 38NM in another. Defense related literature is replete 
with such examples. In the absence of a definitive classified source, what rule of thumb can be applied to 
discriminating among competing 'truths'? 

4.2.1.1 Conflicting Evidence 

Along with simple error, deviations in data values appear to proceed primarily from two sources. Per- 
formance characteristics are observed under a variety of conditions. Factors such as weapons load, mis- 
sion profiles, estimates of combat duration and loiter time all contribute to the measurement of a variable 

like ground attack radius. Even seemingly straightforward characteristics (e.g., combat weight, thrust-to- 

2 
weight ratio, wing loading) can be calculated from different but oiten unspecified bases. Except in classi- 
fied technical publications, it is rare that these conditions are cited. Even when they are, the conditions 

tttttttttttttttt+tttt 
2 



Analogous considerations apply to other types of data observations as well. I 
counted upon initiation (SI PR I) or upon consummation (AC DA)? 

- 51 - 



s an arms transfer 



are invariably unique to a particular case or to a small family of cases. Consequently, it is virtually 
impossible to identify values for a variable down an entire list of cases which were similarly observed. 
The second source of deviation stems from the difference between design goals and realized operational 
performance. With newer systems especially, the lack of an established performance history appears to 
leave the field open to 'best case' analysis and some measure of speculation. 

4.2.1.2 Resolving Contradictions 

There is no neat method for unravelling the resultant web of uncertainty, but its grasp can be loosened if 
the collector recognizes the sources of variation and attempts to level the playing field. In this study, no 
one source was viewed as 'gospel'. Values for a system or inventory variable were collected from several 
sources, along with information on measurement criteria when presented. When values conflicted, meas- 
urement conditions were examined if available or estimated if not. The value was selected which most 
closely approximated the weapons and fuel loads and operational settings deemed likely in regional com- 
bat. Even when data did not conflict, observation conditions were reviewed or estimated to assess their 
correspondance to the regional employment environment. If deviations appeared substantial, values were 
adjusted accordingly. Once the basic data had been sifted, mathmatically derived values for variables such 
as combat weight, wing aspect ratio, thrust-to-weight ratio, fuel fraction, and wing loading were recom- 
puted using the formulae described below. This procedure generated a set of data bases in which the 
sources of deviation had been minimized and in which the biases, if any, were at least consistent. 

4.2.2 Filling Gaps 

4.2.2.1 All the Numbers 

Missing data are the bane of the quantitative researcher. Missing data adulterate statistical results and cast 

suspicion on final values computed for each case. As Joshua Epstein notes, the researcher has two 

options when confronted with missing data. 

First, one can stop, throw in the towel, and regress to bean counting. Or, one can pro- 
ceed like a rational animal: by fighting off the conditioned response that perfect measure- 
ments are necessary to make a reasoned judgment on bounds; by drawing the most intel- 
ligent inferences one can from the data that are available; and by varying one's 
assumptions so that the consequences of irreducible uncertainty may be gauged. 

These principles were, of necessity, applied liberally in the research at hand. 

After initial data collection and review, missing data dominated some variable columns and affected 

all. Across the spectrum of variables and cases, missing data represented over 20% of the observations, 

with higher concentrations in certain key variables and sets of cases. Some of the variables for which 
ttttttftttttttttttttt 

3 

There is a horizontal dimension to this dilemma as well. Have the values for unique but related vari- 
able for the same system been measured under the same circumstances? 

Epstein, Measuring Military Power, pp. 145- 146. 

- 52- 



more than 50% of the data were missing were dropped in the belief that their explanatory power was neg- 
ligible or was captured just as well or better by other variables (e.g., combat range, stall speed). However, 
there were no suitable sustitutes for the explanatory power represented by others such as specific excess 
power, instantaneous rate of turn, and combat radii. From a case perspective, data were most often 
missing for Soviet and some European produced aircraft, a variety of target acquisition systems, and 
countries with Soviet dominated inventories. Whether missing data represented a major portion of the 
observations on a variable or were limited to just a few, the task was the same - to fill in the blanks 
through 'intelligent inference'. 

4.2.2.2 Analogous Comparison 

The inferential process moved through three phases in ascending order of complexity and descending 
order of certitude. First, cases with missing data were reviewed to suggest analogous cases for which data 
on a given variable might be available. This procedure was particularly fruitful in filling in gaps in obser- 
vations on individual models of a family' of aircraft. For instance, if the service ceiling for the MiG-23B 
were cited in an authoritative source, but none were listed for the MIG-23E, the value for the MiG-23B 
was assumed to apply to both models. In a slightly broader extension, a 'signature' characteristic of a 
generation of equipment from the same producer was assigned to cases missing that value. For example, 
aircraft fielded by Dassault- Breguet during the 1970's on which Combat 'G' Limit data were available all 
showed the same value (7.33). That value was extended to aircraft from the same producer on which 
definitive information was not available. 

4.2.2.3 Regression Analysis. 

The relatively innocuous analogical process was successful in reducing the body of missing data consider- 
ably, but some troublesome although scattered gaps in key variable observations remained, notably those 
pertaining to combat radii and maximum speeds. A statistical inferential tool, regression analysis, was 
employed to fill these gaps, with the results modified by expert judgment. Pearson correlation coefficients 
were inspected to identify variables pairs which displayed strong statistical affinity. Those pairs which did 
not also intersect functionally (statistical artifacts) were discarded. The remainder were plotted to deter- 
mine the statistical significance of their relationship and to ascertain if the relationship were distorted by 
extreme values (outliers). In the penultimate step, the variable pairs were subjected to regression analysis 
to define the predictive potential of one to the value of the other and to derive suitable prediction equa- 
tions. Finally, the regression equations were employed to predict dependent values for all cases, and the 

t+tttttttttttttttttrt 

There is always a danger of overlooking a differentiating factor, however. F-15A/B's had a 'G' Limit 
of 7.33, but sensor changes in the C/D model permitted an increase in the placard limit to 9.0. 

Some tests were also conducted using two, three, and four predictor variables in multiple regression 
equations. This technique is arguably more powerful than the variable pair approach and bears further 

- 53 - 



results were compared to those cases with known values on the dependent variable to judge the equation's 
efficacy. 

To illustrate the process, the value for sub-sonic area intercept was missing for 21 fighters. One pos- 
sible variable from which the unknowns could be predicted was ground attack radius. In those cases in 

2 
which values for both variables were known, they showed a positive correlation (r) of 0.88037 and an R"" 

of 0.77505, suggesting good explanatory potential. A scattergram reinforced the picture of a strong posi- 
tive correlation not unduly influenced by extreme or outlying cases and indicated the variables would dis- 
play a siginifcant positive relationship in all but one of 10,000 cases (F = .0000). A regression problem 
with air intercept radius as the criterion (dependent) variable and ground attack radius as the predictor 
(independent) variable was formulated. The results are depicted in Table 4. 1 



Table 4.1: Predicting Air Intercept Radius 

Ground Attack Radius as a Predictor 

Multiple R .88037 F =75.80131 

R Square . 77505 Signif F = . 0000 

Adjusted R Square . 76483 

Standard Error 54. 67714 

Variables in the Equation 

Variable B SE B BETA T SIG T 

Ground Atk Rad .78994 .09073 .88037 8.706 .0000 
(Constant) 232.49856 39.61341 5.869 .0000 



A solution for the unknown value can be derived by substituting the known value and data from the 
regression equation into the equation for a straight line: a = by + k, where (in this case): 

a = Air intercept radius 

b = Slope of the regression line 

y = Value for ground attack radius 

k = Value of the constant (intercept point) 
The result of the computation is a predicted value for air intercept radius which, on the average, should 
fall within plus or minus 55NM (the standard error) of the actual value. When the equation was applied 
to all cases, and predicted compared to known values, predicted and actual values correlated closely in the 
middle of the data set, with error as little two nautical miles. However, the observed error increased 

exponentially toward the upper and lower extremes, resulting in two predictions (of thirty-eieht) that were 

ttttttttttftttttttttt 

exploration. 

- 54- 



in excess of 120NM off. The average error was 16%, and the direction of error was almost equally dis- 
tributed between high (52%) and low (48%) predictions. In light of these observations, the predicted 
values for the 21 unobserved cases were scrutinized individually and modified or estimated by another 
method if distortion were suspected. This cautionary note notwithstanding, the regression technique, 
when tempered with expert judgment, proved a most productive and reliable tool for filling data gaps. In 
all, over 30 repression equations were developed and employed, closing all but the most persistent voids in 
the data sets. 

4.2.2.4 Estimative Analysis. 

Analogy and regression work well as gap fillers when values are missing for a limited number of cases and 
are not disproportionately concentrated on a particular variable or class of cases. Unfortunately, data on 
several weapons performance variables, two employment related variables, and one class of inventory 
variable were almost universally unavailable through open data sources. Careful estimation of values 
appeared to be the only practicable solution. Estimation in this context does not suggest an arbitrary 
assignment of values simply to provide grist for subsequent evaluations. To the contrary, care was taken 
to involve ouside experts and other researchers' techniques in bringing the values as close into line with 
assumed reality as possible. By definition, the estimation process incorporates a margin of error. Its 
methods are not rigorously scientific, nor are its results exact. The fact that the element of uncertainty 
may be transmuted into substantive results does not invalidate the overall assessment technique. In fact, 
the ultimate combat potential computations are designed in such a manner as to permit the painless 
replacement of estimated data with actual (or better estimated) values if and when they become available. 
Those variables or classes of cases for which the bulk of the values were estimated are clearly identified in 
the following section along with notes on the estimative techniques employed. 

4.2.2.5 Expert Review. 

In the final analysis, there is no substitute for informed judgment. So, the final data bases were submitted 
for review to two senior fighter pilots (airframes, configuration, air-to-air missiles, and guns), an experi- 
enced weapons system operator (target acquisition systems), and a regional intelligence officer (invento- 
ries). While their reviews were necessarily cursory, they did identify a number of values winch they knew 
to be in error or suspected to be out of tolerances. Additionally, all variables were analyzed using univar- 
iate statistical techniques to flag values which appeared out of character for the data set. Suspect values 
were double checked and replaced if warranted. This process brought the data bases to the level of com- 
pleteness required by an investigation of this type while also purging them of random and systematic 
error. 



55 



4.3 Sources and Methods 

4.3.1 General Comments. 

The data collection process is, regretably, not nearly as cleanly systematic as the resulting well ordered 
data bases might suggest, nor are the results necessarily definitive. It is incumbent on the researcher to 
make the collection process as transparent as practicable so that the user can arrive at his or her own 
conclusions concerning the information's validity. With this precept in mind, the following paragraphs 
highlight the primary sources used in compiling the research data bases, identify equations used to calcu- 
late derivative values, and provide explanatory notes on the techniques used to estimate values for vari- 
ables which were largely unobserved. Compiling values for many of the variables was relatively fortliright 
and non-controversial, and the associated explanations self-evident to the vast majority of readers. These 
will not be addressed individually. Nor will each case in which analogous examples or regression pre- 
dictions were employed to fill discrete data gaps be discussed. Rather, attention will be focused on those 
variables and classes of cases considered noteworthy or potentially contentious. 

The following subsections are ordered in consonance with the variable grouping scheme outlined in 
Chapter 3. Primary data sources and mitigating factors are discussed in a lead-in paragraph, followed by 
specific comments on the derivation of values for those variables which might provoke some question. 
The full data sets are reproduced in Appendices B through D. All were compiled using SPSSX coding 
conventions, so some of the descriptive information is relatively cryptic. Full variable names, measure- 
ment units, and value descriptions are provided in the formal file description documents in Appendix A. 

4.3.2 Airframe Performance Data. 

4.3.2.1 Sources 

Airframe performance data were culled from numerous publications. Various editions of Jane's All The 
World's Aircraft constituted the primary source, closely followed by Gunston and Spick's Modern Air 
Combat. Other specialized publications such as Cordesman's Jordanian Arms and the Mideast Military 
Balance and The Gulf and the Search for Strategic Stability, and the Department of Defense's Soviet Mil- 
itary Power were also invaluable. A number of periodicals proved fertile sources, particularly on later 
model systems. The most prominent of these were Aviation Week and Space Technology, Interavia, 
Armed Forces Journal International, and Air Force Magazine. Last but not least, some information was 
obtained directly from American, British, and French aircraft producers' literature and informally from 
numerous of the author's acquaintances who had direct experience with particular systems. 



- 56 



4.3.2.2 Comments 

The general principles which were applied in sorting through the data and selecting specific values for 
entry into the data base were described previously. Some explanatory information on variables of interest 
is provided below. The aeronautical formulae cited were lifted from one of three documents: the U.S. Air 
Force Fighter Weapons School Instructional Text, Basic Aerodynamics; Gunston and Spick's Modern Air 
Combat; and Legrow's Measuring Military Capabilities for Military and Political Analysis. 

Aircraft Designator. Because of coding protocols, aircraft names had to be condensed in most 
instances. The aircraft name is followed by the variant designator. In those instances in which an aircraft 
has undergone major modification for a particular recipient, an additional letter has been attached to the 
variant code corresponding to the first letter in the name of the operating nation (e.g., MIRIIIEI for the 
Israeli modified Mirage HIE). For Soviet aircraft, the name corresponds to the Soviet designator (e.g., 
MiG-23). The variant designator is derived from the NATO classification (e.g., B) which is more com- 
monly recognizable than the multi-letter Soviet model designators. 

Wing Span and Wing Surface Area. Values were for the most part taken directly from source docu- 
ments. In the case of variable geometry wing fighters, the values were selected which reflected most likely 
wing sweep during combat employment. 

Wing Aspect Ratio. This measurement was recalculated for each aircraft from data entries for wing 
span and surface area using the formula: AR = b /S, where, 

AR = Wing Aspect Ratio 

b = Wing Span 

S = Wing Surface Area. 
Combat Weight. Values for all aircraft were recalculated to reflect a likely combat weight. The 
computation added half the internal fuel weight and the weight of a normal combat weapons load to the 
aircraft's empty weight. All multi-role fighter weights were computed in the air-to-air role. Weapons 
weight for air-to-air and multi-role aircraft was derived directly from the weight of the air-to-air missiles 
identified in the aircraft configuration file. Weapons weight for all air-to-ground fighters was calculated at 
half of maximum ordnance load and that of bombers at full ordnance load. This technique was used 
because most fighters will rarely fly with a full complement of air-to-ground ordnance, particularly when 
range is a compelling consideration, as it would be in most Middle Eastern scenarios. 

Combat Wing Loading. Values were computed from file data using the formula: WL = W/S , 
where: 

WL = Combat Wing Loading 

W = Combat Gross Weight 

S = Wing Surface Area. 

- 57- 



Fuel Fraction. The weight of internal fuel as a percentage of the clean (without weapons) take-off 
weight of an aircraft. 

Thrust- to- Weight Ratio. The ratio of installed (with afterburner at sea level) thrust to combat gross 
weight. 

Specific Energy at Altitude and at Sea Level. Depicts total aircraft energy (kinetic plus potential) 
under specified conditions of flight according to the formula: E = h + V /2g, where: 
E = Specific energy under the given condition 
h = Altitude (service ceiling or sea level) 

V = Maximum Airspeed at altitude or sea level 
g = Force of gravity. 

Specific Excess Power. Authoritative values for specific excess power were available in open sources 
for less than 20% of the aircraft in the data set. The small number offered scant promise for application 
of the analogical or regression techniques. A less rigorous and less reliable estimative approach was called 
for. Specific excess power measures an aircraft's relative ability to change its energy state. Thus, it must 
be measured from a common energy state described in reference to altitude, velocity, and attitude. In 
deference to available data, these were stipulated as 10,000ft, .9Mach, and 1G respectively. Specific excess 
power can be calculated by the following equation: P = V(T-D)/W, where: 
P = Specific excess power 

V = Velocity (.9 Mach) 

T = Maximum thrust available 

D = Drag 

W = Combat gross weight. 

Thrust and weight data were readily available, but information on drag is rarely published in unclas- 

7 
sified sources. With expert assistance, drag was 'back-calculated' for those aircraft for which P was 

known and was compared to variables observed for all aircraft. Wing surface area and combat weight 
appeared to oiTer the most explanatory promise. With too few observations to conduct a proper regres- 
sion analysis, several calculations were tested until the equations which most accurately predicted to the 
known values were isolated. These equations were applied to establish values for drag. P was then cal- 
culated for all cases. The results were largely satisfactory, although not precise, with one exception. Val- 
ues for Soviet and earlier generation aircraft were larger than deemed reasonable. This overestimation is 
believed to result from the fact that the estimates were primarily derived from observations on late-model 

U.S. aircraft which are generally aerodynamically cleaner than their Soviet counterparts and earlier cenera- 

ttttttttttttttttttttf 

7 

Colonel Michael Nelson was invaluable in untangling the technical web associated with this and other 

aeronautical questions and in suggesting alternative approaches to estimative hurdles. Without his 

help, it is unlikely they would have ocen cleared. 

- 58- 



tion aircraft. That quality was not captured in the estimate. To compensate, estimated P values for 
Soviet aircraft and early generation U.S. and European aircraft were adjusted downward on a case-by-case 
basis, with a maximum adjustment of 10 percent. 

Maximum Instantaneous Rate of Turn. Data were available on only a handful of cases through 
unclassified sources, and the conditions under which they had been observed were infrequently cited. 
Given these tenuous circumstances, it was obvious that instantaneous rate of turn would have to be cal- 
culated independently not only to fill in the blanks but also to create a common plane of comparison. An 
aircraft's best instantaneous turn rate is calculated through the equation: (0 = K (G /V ), where: 

(0 = Instantaneous turn rate 

K = A constant which converts radians per second to degrees per second and accounts for the 

value of gravity 

G = Maximum radial G 

V = Comer Velocity. 

A. 

Two terms need further explanation. Radial G is the vector which defines the plane of a turn and is 
equal to the square root of cockpit G (G ) minus one. Since the goal is to calculate the aircraft's best 
turning performance, G was set at the aircraft's combat G limit (placard limit) which represents the 
maximum gravitational force the aircraft's structure is built to withstand. Corner velocity (V ) is the 
speed at which an aircraft can turn most efficiently, the velocity at which available G is exhausted. 
Available G increases as the square of velocity up to the structural G limit of the aircraft (G,x Once that 
limit is reached, available G is constant, and increasing velocity results in a decreasing rate of turn. To 
grasp an aircraft's best turning performance, it is first necessary to determine its corner velocity. 

The immediate problem was that data on V is rarely published. Consequently, the author had to 

A 

rely on an expert-assisted estimative procedure. Two known variables, wing loading and thrust-to-weight 
ratio, were identified which generally correlated to the V values derived by decomposing published rate of 
turn data according to the above equation. An admittedly unscientific procedure was evolved which pre- 
dicted to known values fairly accurately. This method was used to predict V values for all aircraft. 
These, in turn, were inserted into the rate of turn equation, and estimated instantaneous turn rates gener- 
ated for all cases. While this technique was the best which could be improvised, the resulting estimates 
range to the high side. However, the bias appears consistent, so the results should not distort further 
applications unduly. 



- 59 



4.3.3 Target Acquisition Systems. 

4.3.3.1 Sources 

Data for this set was considerably less profuse than was available for airframes. In addition to the All the 
World's Aircraft, two other volumes from the Jane's series provided invaluable data: Avionics and Weap- 
ons Systems. Information was also gleaned from many of the periodicals cited above and from a few 
producers. Finally, The Analytic Sciences Corporation's excellent study, The TASCFORM ' Method- 
ology: A Technique for Assessing Comparative Force Modernization served as the template for assigning 
nominal values to those variables for which interval measures were not appropriate. Many of the values 
were subsequently altered to accommodate a different computational methodology, but the initial contri- 
bution was vital. 

4.3.3.2 Comments 

Several general notes concern the cases themselves. The aircrew has an inherent target acquisition 
capability irrespective of the systems installed. This was accounted for by creating a case called 'Visual', 
the values for which reflect an aircrew's unassisted ability to detect a target. Values on this case were 
developed through aircrew interviews and should be viewed as representative rather than absolute. Sec- 
ond, sufficient data were not available to differentiate among various laser ranging and target designation 
systems comfortably. Consequently, they were treated as generic cases, with values drawn from the limit- 
ed data currently available. Third, authoritative data were not found on the radars installed on the latest 
Soviet fighters (Flanker, Fulcrum, Foxhound) or on the infrared search track systems on two Flogger 
variants. However, several articles speculate that their performance characteristics are essentially similar to 
those of some Western systems. The radars are identified in terms of the aircraft (e.g., 'FLAN RAD'), 
with the performance data adapted from the putatively analogous Western system. The infrared search 
track systems are differentiated by the letter of the Flogger model in which they are installed (e.g., 
IRSTSB). Finally, in a few instances, the measurement variable is not entirely germane to a particular 
system (e.g., output power for visual acquisition or infrared systems). In these, a dummy value was 
derived from a regression equation which calculated the relationship between range and output power for 
the radar systems. These cautionary comments aside, the target acquisition system data base captures the 
bulk of the key attributes relevant to air combat. 

Range-High Target and Range-Low Target. Data were collected which to the greatest extent possible 
reflected the system's capability to detect a fighter-sized target (5m") while in the search mode. Adjust- 
ments were made to the data when measurement under conditions other than these was indicated. The 
two measurements were included to account for superior target detection potential accruing to a system 



60 



which can distinguish a target while 'looking down' into ground clutter. Systems having this capability 
had data entered for both variables. Air intercept radars capable only of acquiring targets at the same or 
higher altitudes were measured only on the 'High Target' variable, while air-to-ground radars had data 
entered solely on the 'Low Target' variable. 

Data Points. The categories of significant data which the acquisition system could relate to the air- 
crew or weapons computer relative to the target were enumerated for each case. These include range, 
bearing, altitude, and airspeed. Data were entered as available from system description and imputed from 
other system characteristics when not. 

ECCM Capability. The scoring scheme was adapted from the one developed by The Analytic Sci- 
ences Corporation. Values ranged from 0.7 for a system with a high susceptibility to electronic counter- 
measures to 1.1 for a system with very low susceptibility. 

4.3.4 Air-to-Air Missiles. 

4.3.4.1 Sources 

Performance data on air-to-air missiles was drawn largely from Jane's Weapons Systems along with many 
of the aforementioned periodicals. Additionally, Gunston's Modern Airborne Missiles proved a most 
valuable source document. As was the case with target acquisition systems, The Analytic Sciences Cor- 
poration study provided a thoughtful matrix for extracting differentiating values for classes of nominally 
described variables. 

4.3.4.2 Comments 

Terminal Guidance Mode. Descriptive values (e.g., 'SARH' for semi-active radar homing) were 
entered in the data base. Associated values were assigned to a separate variable, guidance score. These 
values range from 0.7 for a command guided missile to 1.2 for one with active radar homing. They are 
further differentiated to reflect relative accuracy within class. For instance, an older infrared guided system 
is scored as a 0.9, while a more modem version is rated at 1.0. 

Maximum Range-Head On and Maximum Range-Tail. Two maximum range values were entered to 
differentiate those missiles with all aspect capability from those which can only be launched from the rear 
hemisphere (primarily infrared guided systems). A missile with an all aspect capability is measured on 
both variables; one with a single aspect capability on only one. 

Minimum Range- Head On and Minimum Range-Tail. This variable captures the distance required by 
the system to actuate its guidance system after separation from the launch platform. Criteria for entering 
values is as with the previously discussed variable pair. 

Radars possessing a 'depressed angle' rather than pure look-down' capability were treated as having a 
capability against lower altitude targets, but at attenuated ranges. 

- 61 - 



Effective Range-Head On and Effective Range-Tail. Adjusts the maximum range of the missile to 
account for the rninimum range which must be covered before it is effective. It is computed with a for- 
mula borrowed from the TASC study: R a = R_„ v (1 - R moT /R m i n J, where: 

C I lid. A 1 1 Id. A 1 1 1111 

R = Effective range 

R~,„ v = Maximum range 
max 6 

R~,;„ = Minimum Range, 
mm ° 

ECM Susceptibility . Assignment of values for this variable adheres to the same concepts described 
above, but with the spectrum reversed. In this instance, a value of 0.7 reflects the system with the lowest 
susceptibility, while one of 1.1 marks a system which is highly susceptible to countermeasures such as 
flares, chaff, or electronic jamming. 

Acquisition Mode. Two descriptive values are entered in the data base to indicate if a missile is 
capable of engaging targets at beyond visual range (BVR) or is limited to visual range engagements (VR). 
The descriptions are not associated with a numeric value, but are used to differentiate employment condi- 
tions under the scoring logic which modifies the guidance score according to its pertinence to a particular 
mission type. 

4.3.5 Aerial Guns 

Data for this category were extracted almost exclusively from Jane's Weapons Systems. Some additional 
data were also taken from brochures distributed by producers. A few externally mounted guns were 
included in this data set which is primarily concerned with internal weapons. Pod mounted guns were 
entered to permit their evaluation as a configuration option during weapon system score compilation if 
desired. 

4.3.6 Relational Variables. 

4.3.6.1 Aircraft Configuration Data. 

The sources for the configuration data set were generally the same as cited above, with some notable 
additions. The International Institute for Strategic Studies' The Military Balance was used to identify the 
specific weapons available to a country for installation on its aircraft in a given year. Joshua Epstein's 
book Measuring Military Power was irreplaceable as a source of data on aircraft man maintenance hours 
per flying hour and, more importantly, as a guide on how to go about estimating values for systems on 
which data were not published. In the latter regard, operations analysts at Northrop Corporation's Air- 
craft Division provided insights into framing the estimation problem and practical documentation of esti- 
mation techniques. 



-62- 



For the most part, the entries in this data set are self-explanatory, indicating the presence or absence 
of a class of capability or the installation of a particular target acquisition system, air-to-air missile, or gun. 
Weapons system description documents such as Modern Air Combat catalogued possible or likely config- 
urations. The Military Balance and various articles in periodicals and newspapers offered more definitive 
information on subsystems available to a given country. Finally, some subsystems were deleted from ver- 
sions of an aircraft in deference to political considerations associated with its transfer. For instance, two 
versions of the Tigershark were configured, one with full up systems to included a radar missile and 
sophisticated ordnance release point computer capability (F-20A) and one without (F-20). The latter 
version is figured to be the one most likely to be approved for transfer to a Middle Eastern country like 
Jordan, owing to political sensitivities. A version of the F-16C (F-16CSC) was similarly configured for 
the same reasons. 

The system configurations in this file represent a best estimate which is by no means definitive. The 
values of all of the variables in this file are changeable during the combat potential scoring process. Tliis 
feature permits the user not only to correct entries that might be in error but also to switch subsystems 
and weapons to determine their impact on resultant combat potential. 

Country of Production. In most cases, the entry on this variable reflects the original country of pro- 
duction. No attempt has been made to identify aircraft for which the recipient country might have some 
co-production responsibilities. Similarly, sources of secondary transfers are not singled out. There are a 
handful of exceptions, mostly pertaining to aircraft in the Israeli inventory. When an aircraft has been 
drastically modified by the recipient, the country of production annotation has been revised to reflect its 
largely indigenous nature. 

Navigation Category. The descriptive values entered for this variable categorize the most sophisticat- 
ed navigation system installed on the aircraft. They range from dead reckoning to a global positioning 
system. Not shown in this file are the differentiating values associated with these categories, which come 

into play in the combat potential scoring process. These values are scaled from 0.6 to 1.4 reflecting the 

9 
navigation system's contribution to overall weapons system effectiveness. 

Man Maintenance Hours per Flying Hour (MMHjFH ). Collecting sufficient data on this variable 

was an elusive task. While it suits the purposes of this study perfectly and is described by Epstein as 'the 

standard index of aircraft maintainability in peacetime,' little data is published on it. In fact, authoritative 

data could be obtained on only 21 aircraft, all but two of U.S. manufacture. The problem is compounded 

by the fact that the maintenance hours required vary from year to year, presenting a moving target. 

Because of these factors, it was necessary to adopt an estimative approach to fixing values for this vari- 

ttttttttttttttttttttt 

9 . . .... 

The categories and associated values were primarily developed by Major William R. O'Brien, an F-l 1 1 

Weapon Systems Operator with 15 years experience with aircraft navigation systems. 

- 63- 



able. Epstein makes a solid case for taking this tack, noting that, while the estimated figure might not be 
entirely accurate, it is a viable delimiter of mission generation. 

The MMH/FH value associated with an aircraft is largely a product of two factors: the frequency 
with which maintenace is required and the difficultly of effecting the maintenance. These are most fre- 
quently measured as Mean Time Between Failure (MTBF) and xMean Time to Repair (MTTR) respec- 
tively. There are other intervening variables which some into play, such as organizational maintenance 
concepts, but these will be set aside here. An aircraft's MTBF is dictated in part by the number and reli- 
ability of its subsystems, while MTTR is a product of their number, complexity, and the maintenance 
procedure involved. Deficiencies in any of these areas can be offset by efficiencies in another. For 
instance, newer fighters like the F-20A and the F-16C have multiple subsystems, but the maintenance 
load is ameliorated through the reliability of advanced microelectronics and the pull-out, plug-in concept 
of primary maintenance associated with them. It stands to reason that if MTBF and MTTR were known 
for an aircraft, predicting to MMH/FH would be a fairly accurate process. 

Unfortunately, those data are only marginally more available; so the estimation process has to fall 
back one level and focus on analogous reasoning at the subcomponent level. A 1980 article presented a 
body of data taken from Department of Defense reports which categorized 12 fighter aircraft according to 
their complexity and indicated their respective failure rates, associated workload, and mail maintenance 
hours per sortie. Various articles since then provided similar data on nine additional fighters. Using 
this data as a baseline, the configuration data base and aircraft descriptions were studied to identify those 
aircraft which were similarly appointed and were fitted with subsystems of the same vintage. Aircraft were 
subjectively grouped, and analogous MMH/FH values assigned to those aircraft for which the variable 
was undocumented. Multiple variants of a basic airframe were assigned the same value, unless their sub- 
systems were substantially different. Some allowances were made for discrete reports concerning the reli- 
ability of individual systems. For instance, Jordan and Iraq are reportedly displeased with the maintain- 
ability and supportability of the Mirage Fl, causing values for that aircraft to be elevated slightly. " The 
process worked satisfactorily for the majority of the aircraft in the file to generate data wliich portrayed at 
least some measure of the relative differentiation among the systems. 

No doubt, the resulting values contain many inaccuracies, perhaps some serious. However, these 
need not be debilitating within the context and objectives of the study. The goal is to assess relative 

combat potential, and the values derived via this process do that adequately, albeit imperfectly. It can be 

tttttttfttttttttttttt 

See Epstein, Measuring Military Power, pp. 153-165 for the estimative technique which he employed 
in his study and its justification. 

See Benjamin Schemmer, 'Pentagon, White House, and Congress Concerned over Tactical Aircraft 
Complexity and Readiness'. 

See Cordesman, Jordanian Arms, p. 87 

-64- 






11 
12 



presumed at least that the errors will be no greater than those which might have resulted from picking 
'authoritative' data from a single year. The figures can be challenged individually, but as a whole they 
suit the purposes of this effort. 

4.3.6.2 Relative Utilities 

As noted in the previous chapter, a family of data had to be collected to glue weapon system attributes 
together at their joints. The data had to reflect the relative contributions of these attributes to definable 
mission outputs. The Analytic Sciences Corporation embodied this concept in its computational matri- 
ces. But the specific values (termed 'Weighting Factors') were not suitable for direct adaptation for three 
reasons. First, the TASC computational process differed from the one under consideration for this study 
in several important areas. Attempts to decompose or rearrange TASC's values to suit this study's 
scheme proved unfruitful. Second, the specific sources of the values and the considerations which went 
into them were opaque. Third, the values were predicated on a Central European operating environment. 
Since depicting the influence of the Middle Eastern operational environment on relative combat potential 
is a study goal, greater control over the factors considered in formulating the values for the relational vari- 
ables is imperative. 

Expert Survey Concept. The concept underlying the survey procedures employed by LeGrow and 
Jacoby in their explorations of Multi Attribute Utility Technique (MAUT) offered an attractive solution. 
The collective judgment of experts with first-hand knowledge of the phenomena being investigated is a 
valid measure of relative merit, subsuming the myriad of micro -considerations which defy individual 
quantification in an aggregated model. Despite the flaws in the previous applications of MAUT to mili- 
tary analysis outlined in Chapter 2, the survey technique on which it was predicated holds promise if 
questions are focused on a reduced basket of relationships with which the respondants are all intimately 
familiar and which could be considered at an intellectually more malleable level of abstraction. 

Survey Formulation. Having been identified previously (Chapter 3), the junctures on which relative 
utility values were needed were organized into a tabular structure which graphically outlined the relation- 
ships to be evaluated. The basic questionnaire is included in Appendix C. A chart was prepared for each 
air weapons system component which arrayed the component's key attributes against the four combat 
missions being evaluated without reference to a particular system. The respondant was asked to make 
zero-sum determinations on the relative contribution of each attribute to combat success in each category 
of mission. The subcomponents having been scored, the respondant was asked in another chart to relate 

them under the same conditions. A final chart requested a similar rating of the air weapon svstem, oper- 

ttttttttttttttttttttt 

Between the beginning of 1976 and the end of 1977, the mean time between failure rate for Ihc F- 15 

'ue 



between tne oeeinning ot iy/q anu tne end oi iy//, tnc mean time netween iaiturc rate ior tne i-i 
increased from 0.76 to 1.30, bringing its MM 11/ FH value down to 41. Just two years later thai valu 
had dropped further to 33.6. The error resulting from taking a 'snap-shot' of the data could pro\ 
just as fallacious as employing the estimative technique described here. 

- 65- 



ator proficiency, and command, control, communications and intelligence support (C I) contributions to 
success in each mission category. Finally, five questions were included to establish the respondant's sys- 
tem familiarity and fighter and combat experience. These data were used in discriminating among 
responses if substantial disagreement on individual values cropped up. An accompanying letter defined 
the Middle East the the employment region and gave a thumbnail description of a moderate intensity 
(compared to Central Europe) air operating environment. 

Survey Administration. Experienced fighter pilots familiar with flying conditions and combat scenar- 
ios in the Middle East represented the best source of well informed survey judgments. Within the L'.S Air 
Force at least, these are concentrated in Tactical Air Command's 9th Air Force, which serves as the air 
component of the United States Central Command (USCENTCOM). Weapons and tactics officers from 
the HQ 9th Air Force Directorate of Operations, whose primary job is developing combat plans and tac- 
tics for the Middle East/Southwest Asia contingency operations, were requested to participate in the sur- 
vey, along with weapons and tactics officers from two fighter wings with USCENTCOM contingency 
commitments. Officers currently flying six different types of aircraft (A-7, A- 10, F-4, F-15, F-16, and 
F- 111) were included in the survey. Twenty-four are pilots, with one an F- 111 weapons system operator. 
They reported an average of almost 2000 hours total fighter time (high:4600, low:325). Thirteen had 
accumulated an average of just over 500 combat hours, and eleven had some flying experience in the 
Middle East. All had flown in exercises which simulated a Southwest Asia combat environment. So that 
scenarios and objectives would be well understood, points of contact in each organization surveyed were 
briefed and asked to select those officers who would generate the most thoughtful responses. 

Survey Results. Data entered into the questionnaire tables were reformatted into an automated file as 
values for the previously described relative utility variables. They were processed to determine the distri- 
bution of data for each variable and to extract relevant statistical information such as their mean, maxi- 
mum, rninimum, and median values and to establish a range of responses. Responses for 57 of 76 vari- 
ables showed strong central tendencies, with median and mean values within 10 percent and with response 
ranges of 40 points or less. Responses for only 10 variables showed a deviation of more than 10 percent 
between the median and mean values. Of the 19 variables which displayed a range of values in excess of 
40, the range for 15 could be reduced to 30 points by the removal of 3 or fewer of the extreme responses. 
The categories of variables which showed the most pronounced divergencies of opinion were those related 
to relative utility of radar guided air-to-air missiles, to that of precision guided air-to-ground munitions, 
and to that of target acquistion modes. Additionally, a lesser breadth of opinion was registered concerning 
the relative utilities of target acquisition systems and weapons payioads in the air defense and air superi- 
ority roles. While these divergencies tarnish the aura of the 'collective wisdom' imputed to the mean or 



- 66 



median values somewhat, they realistically mirror alternative positions often taken in arguments concern- 
ing weapon system development, employment, and outfitting priorities in the tactical community. These 
incidental disagreements aside, the survey results are sufficiently cohesive to produce relative utility values 
which might not hit the mark but which will be very close to it. 

One of two values (mean or median) can be selected as a measure of central tendency to extract a 
typical score from data sets such as these. The mean is generally regarded as the best descriptor and is 
preferrable to the median if the data set is not highly skewed. Only 19 of the 76 variables in this data 
set had skewness values of 0.5 or greater, and all of those were reduced to less than 0.5 through the 
removal of 4 or fewer outlying cases. This procedure was implemented. The resulting relative utility val- 
ues are displayed in decimal form in the tables in Appendix C. While these values will be used for the 
remainder of this study, the scoring procedure is designed so that they can be easily altered by another 
user to reflect a different viewpoint or the different demands of another employment environment. 

4.3.7 Air Inventories. 

4.3.7.1 Sources 

The combat aircraft inventories of the 22 nation study set were compiled from published air orders of 
battle (AOB's) for 1984 and 1985 and supplemented with annual projections through 1990. Primary 
source documents for the established inventories were the International Institute for Strategic Studies' The 
Military Balance , Interavia's Air Forces of the World, and the Jaffee Center for Strategic Studies' The 
Middle East Military Balance. Fragmentary data provided in these publications were also used in devel- 
oping force projections through 1990. Several periodicals were essential in the latter effort. These includ- 
ed Aviation Week and Space Technology, Jane's Aerospace Weekly, and The Air Force Times. Addition- 
ally, projected acqusition information was extracted from two automated files, the Arms Transfer Event 
Data Base produced by Third Point Systems Corporation and the Aerospace! Defense Markets and Tech- 
nology data base compiled by Predicasts Terminal Systems. Information on variables concerned with the 
quality of the maintenance forces was drawn from an automated version of the World Military Expendi- 
tures and Arms Transfer Data Base provided by the Arms Control and Disarmament Agency and from 
the World Bank's World Development Report 1985, the Central Intelligence Agency's The World Fact- 
book, and JCSS's The Middle East Military Balance. Complete air order of battle (inventory) listings are 
included in Appendix D. All inventories reflect the end-of-year totals for the respective calendar year. 
Thus, the 1987 inventory figures represent estimates oi the aircraft wluch would be possessed in Decem- 
ber, 1987. 

tttttfttttttttttttttt 

See Blalock, Social Statistics, pp. 69- 70. 

- 67- 



4.3.7.2 Comments 

Data 'Smoothing' . Looking to future acquisitions, data were 'smoothed' to reflect logical entry into a 
country's inventory when no specific delivery schedule had been reported. The procedure broke blocks of 
ordered aircraft down into unit sized increments and spread these over the delivery period. Aircraft were 
treated as operational when sufficient numbers to constitute a unit were on hand. To preclude the 
erroneous impression of ever-expanding inventories, aircraft which would be made obsolete by newer 
acquisitions were decremented as functional replacements became operational. This technique might 
provoke controversy, but it is logical in light of the limited absorptive and support capabilities of the 
nations in the set. Decrements were not enumerated on a strict one-for-one basis, but were forecast as 
functional conversions at the unit level. 

Acquisition Estimates. Estimative techniques were also employed to project possible acquisitions for 
those countries on which scant planning data were available in open sources, particularly for those coun- 
tries which are Soviet clients. Though virtually no information was available concerning their longer 
range air modernization plans, it is highly unlikely that some modernization will not occur, particularly in 
light of the recent introduction into Soviet forces of four new fighters. Here the procedure was to review a 
country's acquisition track-record, identify the relative spacing between new equipment acquisitions, and 
forecast the receipt of later model Soviet equipment. Without access to classified intelligence sources, the 
resultant inventories in the post- 1986 period cannot be viewed as definitive, but they certainly represent 
one potential course of force evolution for countries like Syria, Libya, Iraq, and the PDRY. 

Operationally Available Rate Estimates. Without classified data, it was impossible to determine pre- 
cise operationally available rates (OAR) for countries and systems. Even at the force level, data had to be 
estimated based on an extrapolation from historical anecdotes. . Historical data were evaluated in the 






context of a nation's military investments and assumed logistical capabilities to develop estimates of force 
level operational availability. The values ranged from 0.9 for Israel to a low of 0.3 for Libya. 

Maintenance Personnel Estimates. No authoritative data were documented to establish the actual 
number of personnel available to perform primary maintenance on aircraft possessed by the nations under 
study. Since values for this variable are integral to the formulation of sortie generation boundaries, an 
estimative approach was dictated. Reviewing data on Lnited States' and Soviet forces in Europe, Epstein 

calculated that approximately ten percent of total assigned air force strength accomplished the direct air- 

1 7 
craft maintenance function. This ratio might not be religiously applied in the Middle East, but it is 

ttttttttttttttttttttt 

This treatment is optimistic, since the actual assimilation period would probably stretch over a year 
or more once the aircraft were in place. However, it is consistent with the concept of portraying an 
outside limit to combat potential. 

Sources included Epstein, op.cit.; Cordesman, Jordanian Arms, The Gulf and the Search for Strategic 
Stability, and 'Lessons of the Iran-Iraq War'; and Staudcnmaicr, 'Iran-Iraq (1980 - ) among others 

- 68- 



likely that most of the nations in the region have borrowed similar personnel allocation concepts from 
their respective patrons. 

In lieu of more explicit data, the above mentioned source documents were reviewed to extract infor- 
mation on known national air force manning in the base years (1984 and 1985). Ten percent of total 
manning was assumed dedicated to direct maintenance. In the case of Israel, mobilized personnel aug- 
mented the active contingent. The number of estimated direct maintenance personnel was divided by the 
number of operational combat aircraft to identify the maintenance man to combat aircraft ratio which 
obtained in the base years. Iran presented a special problem because estimates on air force manpower and 
operational aircraft in the base year were admittedly speculative. Consequently, the maintenance man to 
combat aircraft ratio observed in 1979 was used, reflecting a more reasonable organizational allocation of 
manpower. Data on Lebanon were likewise tenuous, showing an exceptionally high ratio. Since the 
Lebanese Air Force is, for all intents and purposes, non-functional, this anomaly is not significant. 

Future year projections were made by applying this ratio to forecast inventories. Ratios ranged from 
lows of below 1.5 (Libya, South Yemen) to highs in excess of 7 (Israel, Syria, Oman, Sudan, Iran). The 
Iranian ratio was atypically high (22) because of the minimal numbers of operational aircraft available. 
Since sortie generation calculations are also limited by the numbers of airframes available, this drastic 
deviation from the norm would have little actual impact on combat potential estimates. 

Quality of the Maintenance Force. Data on the motivational variables identified in Chapter 3 were 
readily available. Rather than taking a 'snapshot' of a base year, data were assembled as a ten year aver- 
age, predicated on the belief that motivational attributes and their impacts on personnel attitudes evolve 
over time. The technological adaptability variables were drawn from 1982 (percentage of age group in 
secondary school) and 1984 (literacy rate), indicating the relative literacy and educational background of 
personnel who would be available for military service in the subsequent study period. It must again be 
emphasized that these variables are 'soft' surrogates for the phenomena being studied and that this data set 
was compiled for illustrative purposes only. The force quality modifiers developed from it will be applied 
off-line to illustrate their potential impact and should in no way be regarded as definitive. 



tt+tttttttttttttttttt 
17 



18 



For a review of his supporting data, see Fpstein, op.cit., pp. 203-207. 

This assertion was validated in small part by a conversation with an aircraft maintenance officer from 
one Middle Eastern country who stated that personnel to aircraft ratio goals were derived from the 
U.S. model. He also noted that few of the countries with which he was familiar in the region had 
attained them. 

- 69 - 



4.4 Protest and Progress. 

Those readers reviewing the data bases provided in Appendix B and Appendix C will undoubtedly identify 
variable values they believe fallacious. Just as surely, these occassional factual errors will provoke what 
Epstein terms the, 'storm of affronted protest,' which prevails when explicit judgments on numbers are 
made. But those judgments had to be made if the analytic process were to progress. The data are essen- 
tial, and every care has been taken to ensure their accuracy. The exhaustive data lists are reproduced pre- 
cisely so that technical experts can draw informed conclusions as to the relative reliability of the study's 
substantive findings. It is important to note that, while differing individual values might influence the 

outcome of specific combat potential computations, their impact will be discrete and predictably marginal 

19 
and the methodology undergirding them unaffected. 



ttttttttttttttttttttt 

19 

Epstein cautions against analytical timidity when forced to employ data which might be open to 

question: 'Nor should anyone be cowed out of analysis by pseudo scientific demands that an inher- 
ently illusory certitude be demonstrated.' Epstein, op'.cit., p. 146. 

- 70- 



Chapter 5 
DATA REDUCTION 

5.1 Criteria 

Despite the economies applied in the variable selection and data collection processes, the sheer volume 
and differentiation of relevant data exceed manageable proportions. The derivation of aggregated values 
or scores which efficiently measure each of the critical attributes is pivotal in transitioning from raw data 
to a workable force level model. The data reduction process must adhere to many of the same considera- 
tions enumerated in the discussion of variable selection criteria in Chapter 3. While parsimony is a prime 
concern, it cannot be achieved at the expense of incomplete representation of the combat relevant facets. 
Conversely, no one facet should be asymetrically represented, either directly or indirectly. In addition, the 
creation of a relational scoring model presupposes a common mathmatical scale on which all variables are 
measured. Otherwise, the higher level computations are distorted by the varying native scales. To com- 
plicate the problem further, the level at which the values are measured must be appropriate to their appli- 
cation. Composite or index variables identified in the data reduction process must, therefore, have ratio 

2 

properties if they are to be subjected to subsequent multiplicative computations. Consequently, a credi- 
ble data reduction scheme must be judged against four criteria. Is it efficient? Is it comprehensive? Does 
it eliminate the distorting effects of disparate measurement scales? Can its products legitimately be entered 
into subsequent computations? The following sections will critically review alternative data reduction 
procedures, propose a procedure which capitalizes on their stong points, and describe its application to the 
data bases at hand. 

5.2 Alternative Methods 

Basically, the task is to create an indexed value for each relevant attribute which can be measured along a 
homogeneous ratio scale. Among the several methods available, three appear to have most currency in 
projects of this type, each with its drawbacks. These are discussed below, with an estimate of the degree 
to which they meet the above criteria. 



ttttttttttttttttttttt 

For example, if values for speed (1300kts), rate of turn (19.5 degrees/second), and combat range 
(390NM) are simply added, the value for speed accounts for over 75% of the resulting score. 

2 

See Blalock, Social Statistics, pp. 15-22; LcGrow, Measuring Aircraft Capability, pp. 10-20; and Rum- 
mel, Applied Factor Analysis, pp. 222-223 for discussions of level of measurement concerns. 

- 71 - 



5.2.1 Single / Marker / Variable 

One approach is to select a single variable which the researcher believes captures the bulk of the signifi- 
cant variation in an attribute. In effect, this tack is an extension to the most basic level of the concept 
employed in identifying families of variables described in Chapter 3. As with any summarizing technique, 
the choice a single variable discards a measure of the information which describes the attribute. If the 
attribute is monolithic, the loss is negligible. With a multi-faceted attribute, it can be injurious. Single 
representative variables are identified in two manners. The researcher can simply assert that the variable 
captures the essential quality of the attribute. For instance, a previously discussed study stipulated specific 
excess power (P ) as the sole indicator of combat aircraft maneuverability. While P plays a vital role in 
defining energy maneuverability, it fails to account for the equally important aspect of lateral maneuver- 
ability. 

A second technique is to use statistical procedures to isolate a variable the values for which vary 
closely with others linked to the attribute under examination. For instance, the values for maximum 
speed at 36,000ft and at sea level in this data set are highly correlated (r = 0.8278). Similar relationships 
obtain for many variable pairs. Could one variable then be reliably selected to represent the attribute 
defined by both? From one perspective, the procedure has merit, as long as the functional relationship 
between the variables is valid and their correlation is not simply a statistical artifact. The process becomes 
more complicated, however, when more than two variables are associated with an attribute. 

In a variation on the same theme which accommodates several variables, factor analysis can be used 
to define groupings of variables, with the variable having the highest loading selected as representing the 
attribute. For example, Table 5.1 depicts the edited results of factor analysis of 18 of the variables in the 
airframe data set. 

Since Factor 2 includes all of the maneuverability related variables, rate of turn (TURATE) could be 
selected to stand-in for the attribute in subsequent applications. While this technique is more powerful 
than the ones described previously, it still provides a less than comprehensive portrayal of an attribute's 
relative value. 

Of course, selection of a single variable does not solve the measurement problem. The most direct 
solution is to index all observations of the marker variable to a baseline value. In the TASC study, all 
values were divided by the corresponding value for the F-4B, producing a homogeneously scaled data set 
with ratio properties. Variables measured on differing scales could also be converted to standardized 

scores. This method provides an excellent mode for data comparison, but standardized values bv dcfini- 

ttttttttttttttttttttt 

3 . . . 

Note that this application of factor analysis differs markedly from the efforts discussed in Chapter 2 in 

which all variables loading on a factor were incorporated in creating an attribute score. 

One can safely assume ratio properties since all these variables are measured on interval scales with an 
implied although never observed natural zero point. See Blalock, op.cit., pp. IS- 19. 

- 72- 





Table 5.1: 


Airframe Variables Factor Analysis 






FACTOR 


1 


FACTOR 2 


FACTOR 3 


FACTOR 4 


SURF 
CWGT 
SPAN 


. 84577 
.83257 
. 77102 










TURATE 

TWPWR 

LIMG 

PSFLIOO 

CSPD 






. 84657 
. 82407 
. 81333 
. 80217 
. 50170 






STNS 

MAXORD 

GARAD 

FRANGE 

AIRAD 








. 78263 
. 76903 
. 68453 
. 66447 
. 59075 




SCEIL 

LSPD 

SPECENA 

ASPD 

SPECENS 










. 74275 
. 65941 
. 60376 
. 58949 
. 55247 



tion have no natural zero point and, thus, lack the essential ratio property required for multiplicative 
manipulation. 

To recapitulate, the isolation of a single or marker variable to represent an attribute is theoretically 
sound, particularly when solid statistical techniques leavened with expert judgment are employed in the 
selection. The technique engenders parsimony and negates redundancy. However, the marker's explana- 
tory power varies in inverse proportion to the complexity of the attribute being represented. If complex 
attributes suck as manueverability are on the table, a more inclusive technique is called for. The use of an 
indexing scheme to reduce disparate values to a common measurement scale has no major drawbacks, 
eliminating distorting effects and maintaining ratio properties. 

5.2.2 Composite Indices 

To overcome the loss of comprehensiveness inherent in the marker variable approach, some researchers 
'build' composite variables which compress the multiple aspects of a complex attribute into a single value. 
Composites frequently convey meaningful performance related information unobtainable through any 
single component measure. Thrust-to-weight ratio, wing loading, and wing aspect ratio are all widely rec- 
ognized as valid (although not sufficient) indicators of energy maneuverability, turning capability, and rel- 
ative lift respectively. However, composites are legitimate only when their components have a functional 



- 73 



impact on the attribute being represented and their combinational mode reflects an engineering or opera- 
tional reality. There is no inherent fallacy in composite variable construction but its application can be 
crippled through unrealistic variable combination. Rattinger proposed a multiplicative combination of 
speed, payload, and combat radius as a composite measure of aircraft performance. Sherwin and Lau- 
rance demonstrated the inadequacies of this procedure, noting the disproportionate impact of minor vari- 
ations in variable values and its inability to deal with zero values. 

An operationally more legitimate composite variable, 'Payload Utility', was created in the TASC 
study multiplying target acquisition values by the weapons' values. This procedure has considerable 
merit, since the two variables have a synergistic relationship. It is debatable, however, if the multiplicative 
process is a true representation of it. To borrow an anology from another section of the same report, it is 
questionable if a target acquisition system twice as capable as its predecessor were mated with a missile 
system twice as capable as its predecessor that the product would be four times as potent. 

Nonetheless, this type of functionally defensible composite does meet the basic criteria and offers a 
data reduction option under rigorously controlled circumstances. The input variables must be critically 
scrutinzed to ascertain their adaptability to the process, and the computational scheme must reflect 
accepted operational relationships. The variables related to most of the attributes under evaluation here 
do not lend themselves to the composite approach. 

5.2.3 Factor Analysis - A Reprise 

At first blush, factor analysis possesses many of the qualities which satisfy the data reduction criteria out- 
lined above. It is certainly comprehensive in that there are structural limits on the number of variables 
which can be analyzed. It is efficient, since groups of statistically related variables are arrayed into factors, 
each of which accounts for a specified proportion of the overall variance within the data set. This char- 
acteristic permits the researcher to peg the number of factors extracted for subsequent use to the number 
pertinent to the phenomenon under investigation. The factor scoring utility calculates relative scores for 
each case which add the absolute values for the variables in the data set in consonance with their loadings 
on the factor. A single value measured on a common scale is thus generated for each case on as many 
factors as are required to reach the desired level of explanation. Conceptually at least, the major draw- 
back is that factor scores are interval level measures which are not natural candidates for subsequent 
computations involving multiplication or division. This failing is not insubstantial in a model which 

demands aggregation of the cumulative potential of a national inventory. 

tttttttttTTtttttttttt 

See Sherwin and Laurance, 'Arms Transfers and Military Capability', pp. 372-374. Other questionable 
composites include one commonly used in the military community which multiplies payload times 
radius to indicate relative ground attack lethality. 

The procedure is actually more complex and is described in detail in Vogt, The TASCFORM 
Methodology, pp. 2-9 to 2-14. 

- 74- 



Chapter 2 sampled factor analysis based aircraft capabilities studies and highlighted the deficiencies 
encountered in using factor analysis to spring from raw variable values directly to an employment level 
combat potential assessment. In reviewing the factor analyses accomplished by Snider and LeGrow, it 
was observed that the attempts to relate a minimum number of factors to such overarching concepts as 
offensive and defensive capabilities or air-to-air and air-to-ground potential exceeded the reasonable 
bounds imposed by the nature of the technique itself and by the explanatory breadth of the variables con- 
sidered. Exploring the more sophisticated application conducted by the Analytic Assessments Corpora- 
tion, some additional deficiencies were highlighted. Implemented at the systems level, factor analysis 
defines variable groupings which are statistically valid but which often lack functional legitimacy. The 
calculation of scores for performance attributes includes values for variables which are operationally 
extraneous. Factor models incorporate no inherent logic for the aggregation of scores for multiple attri- 
butes (factors). These substantial defects in application aside, the factor analysis technique did demon- 
strate a facility for educing a common scale for the composite measurement of the contribution of multi- 
ple variables to the value of a specific attribute. 

5.2.4 Summary 

Each of the data reduction techniques investigated has significant assets and liabilities. The use of marker 
variables isolated by whatever technique is parsimonious but sacrifices too much explanatory power. The 
creation of composites is a valid but spotty solution of too limited applicability to satisfy the majority of 
analytical requirements in this investigation. Factor analysis offers the most comprehensive solution but is 
ineffective when applied exclusively at the weapon system level. Additionally, its output is not fully ame- 
nable to inclusion in subsequent computations. 

5.3 A Minimalist Approach 

A data reduction scheme which meets the stipulated criteria might seem unobtainable, but the kernel of a 
solution resides in a factor analysis process construed less ambitiously. The programmatic structure 
extruded in Chapter 3 provided a framework in which essential weapon system attributes and their func- 
tional relationships were qualitatively delineated. Therefore, there is no requirement for the simultaneous 
factorial analysis of all variables which pertain to an air weapon system. With attributes already defined 
and linked, data reduction need only be accomplished within the realm of each attribute itself. If all vari- 
ables in the problem were functionally associated with the attribute being analyzed, the derived factor 
scores would be purged of the debilitating influence of irrelevant values. Setting aside the level of meas- 
urement problem for the moment, further elaboration of the minimabst factor analysis approach is war- 
ranted. 



75- 



5.3.1 Variable Reduction 

5.3.1.1 Analyze or Assign 

The first task is to isolate and screen those variables contributing to the attributes identified in Chapter 3. 
To preclude the previously discussed distortions which arise when dichotomous variables are factor ana- 
lyzed, they were excluded from this phase of the data reduction effort and relegated to insertion during the 
combat potential computation phase. The field thus narrowed, there are two alternatives for associatmg 
variables with attributes for factor analysis. Variables could simply be assigned to an attribute group 
based on their functional relationships, or they could be statistically grouped using factor analysis at the 
subcomponent (e.g., airframe, missile, etc.) level. The latter technique offers the advantage of previewing 
statistical anomalies and flagging possible redundancies. Reflecting on the observations made concerning 
earlier studies, reliance on factor analysis alone to accomplish this function could cause more problems 
than it solves. The happy medium is to begin with subcomponent level factor analysis and then modify 
its results judgmentally. 

5.3.1.2 The Airframe Example 

Principal components factor analysis was accomplished for all weapon systems subcomponents. Just 
the procedure to identify and allocate those variables associated with airframes will be described in detail, 
but the same procedure was applied to each subcomponent. Table 5.2 displays the results of the factor 
analysis of 26 variables, with values on 125 combat aircraft which are currently operated or might be 
acquired by Middle Eastern states. 

Five factors were extracted, accounting for 85.9% of the overall variation in the data set. Variables 
loading on the first factor were primarily those associated with aircraft size and weight. The two excep- 
tions were maximum thrust (MAXPWR) and specific energy at altitude (SPECENA). Speed and energy 
related variables loaded heavily on the second factor, along with the variable for wing loading (WLOAD). 
Fuel fraction (FUFRAC) loaded unexplainably on this factor, although weakly. Its expected association 
with range related variables (Factor 4) did not materialize. Those variables measuring energy' and lateral 
maneuverability loaded distinctly on Factor 3, while Factor 4 encompassed range and air-to-ground ord- 
nance related variables. Factor 5, which accounted for just 4.5% of the total variance was limited to wing 
aspect ratio (ARWNG) and wing span (SPAN). The association is unremarkable, since the square of 
wing span is the nominator in the wing aspect ratio calculation. 

Vulnerability Attribute. The next step is to evaluate these statistical results within the context of pre- 
viously identified airframe attributes and examine them for functional relevance and statistical redundancy. 
A key factor in an aircraft's susceptibility to engagement is its size. Bigger aircraft can be detected more 



76 





Table 5.2: Factor 


Analysis - 125 Combat Aircraft 






FACTOR 
1 


FACTOR FACTOR FACTOR 
2 3 4 


FACTOR 
5 


CWGT 

EWGT 

FWGT 

MAXPWR 

MWGT 

SURF 

SPAN 


. 89504 
. 89093 
. 89053 
. 87697 
. 86434 
. 85243 
. 68444 






. 60204 


LSPD 

SCEIL 

ASPD 

SPECENS 

WLOAD 

SPECENA 

CSPD 

FUFRAC 


. 60071 


. 70448 
. 67755 
. 65932 
. 65390 
. 65136 
. 64482 
.55114 






TWPWR 
PSFLIOO 
TURATE 
LIMG 






.85250 
.82569 
. 79935 
. 76382 




FRANGE 

GARAD 

MAXORD 

AIRAD 

STNS 






. 73902 
. 69927 
. 69763 
. 67524 
.67392 




ARWNG 








. 93662 



surely at greater range visually or with radar. An aircraft's empty weight (EWGT) and fuel weight 
(FWGT) are subsumed in the calculation of its combat weight (CWGT), and it has already been stipulat- 
ed that aircraft rarely operate in combat at their maximum weight (MWGT). Maximum power 
(MAXPWR) is irrelevant to the attribute and is assumed to load with these variables because larger air- 
craft require greater power. Therefore, EWGT, FWGT, and MAXPWR were eliminated from further 
processing, leaving the size attribute of the susceptibility to engagement calculation described by the vari- 
ables combat weight (CWGT), wing span (SPAN), and wing surface area (SURF). 

Airspeed! Energy Attribute. The variables which loaded on the second factor were for the most part 
measurements of various aspects of airspeed and energy. Wing loading (WLOAD) and fuel fraction 
(FUFRAC) are the major exceptions, and their inclusion in the factor is a statistical quirk rather than a 
meaningful functional association. Of the remaining six variables, two, specific energy at altitude 
(SPECENA) and specific energy at sea level (SPECENS) are products of calculations in which maximum 

ttt + ttttttTt + tttt++t + 

7 ■ 

Other attributes contributing to susceptibility to engagement are its speed and maneuverability, which 

contribute their own dynamics. 

- 77 - 



airspeed at altitude (ASPD), service ceiling (SCEIL), and maximum airspeed at sea level (LSPD) are ele- 
ments. Since the specific energy variables constitute a more sophisticated measure of the speed, energy 
attribute, they were selected for insertion into the scoring process, along with rate of climb (CSI'Dj. This 
screening eliminated the adverse influence of redundant measures of comparable phenomena and limited 
the remaining field to variables the values of which showed a more normal distribution than their antece- 
dents. 8 

Maneuverability Attribute. Factor 3 variables are all statistically and functionally related to maneu- 
verability (acceleration and turning). The design G value (LIMG) was subsumed in the calculation for 
maximum instantaneous turn rate (TURATE), and the thrust-to-weight ratio value (TWPWR) was used 
in estimating the denominator in the rate of turn equation and is closely correlated (0.98) to specific excess 
power (PSFL100). For the sake of efficiency, TWPWR and LIMG were eliminated from further pro- 
cessing. 

Range I Endurance and Pay load Attributes. The fourth factor encompasses variables associated with 
two airframe attributes: range or endurance capability and payload capacity. It is not illogical that these 
variable should load on the same factor statistically, since aircraft designed to carry large volumes of ord- 
nance are also usually designed to carry it greater distances. More subtly, an aircraft with multiple exter- 
nal stations and and a heavier external load capacity can also carry more external fuel, thereby extending 
its range in certain configurations. However, the simultaneous consideration of payload and range related 
variables in the same same factor scoring module does not satisfy the goal of extracting separate values for 

the range and air- to-ground payload attributes. A composite score for a notional range/payload attribute 

9 
would fail to capture the varying utility of these qualities in different mission roles. 

Consequently, this factor was split into two 'sub-factors' which correspond to the attributes for 
which measurements are desired: air-to-ground payload and range. A further subdivision of the range or 
endurance attribute was also required to accommodate processing considerations. Aircraft with singular 
mission roles (e.g.interceptors or ground attack fighters) had values entered only for the variable, area 
intercept radius (AIRAD) or ground attack radius (GARAD), which corresponded to their mission cat- 
egory. As a result, these two variables are replete with missing values, a fact which causes serious abnor- 
malities in the factor analysis solution and permits factor scoring only if mean values are inserted in place 

of the missing data. The solution was to process air-to-air and air-to-ground aircraft in separate runs. 

tttttttttttfttttttttt 

8 ASPD, LSPD, and SCEIL were skewed -0.256, -1.229, and -0.890 respectively. SPECENA has a 
skewness value of 0.069 and SPECENS one of .447. 

Q . . 

Additionally, it should be remembered that the payload attribute for aircraft accomplishing air- to-air 

missions is already described in terms of specific missiles in the configuration file, making the gross 

measure of carrying capacity irrelevant. 

An alternate was to create separate air to ground and air to air data bases with a variable akin to 
AAC's 'mission radius'. This solution was rejected as being unnecessarily duplicative. 

- 78- 






Multi-role fighters were inserted in each. The final lineup was a factor group representing the air-to- 
ground payload attribute comprised of maximum ordance capability (MAXORD) and air-to-ground ord- 
nance weapons stations (STNS); one focusing on the air-to-air endurance attribute, area intercept radius 
(AIRAD) and ferry range (FRANGE); and one capturing the air-to-ground endurance attribute made up 
of ground attack radius (GAR AD) and ferry range. 

The Orphan Attribute. The fifth and final factor presents an interpretation dilemma. Wing aspect 
ratio is an indicator of relative lift, but it loaded on neither of the attributes which might have been antic- 
ipated, speed/energy or maneuverability. Since the explanatory power of this final factor was negligible 
and did not correspond to an essential airframe attribute, it was dropped. 

5.3.1.3 Target Acquisition Systems, Missiles, and Guns. 

An analogous process was accomplished for each of the other air weapon system subcomponents. To 
avoid repetition, just the high points and anomalies associated with them will be noted. As with air- 
frames, variables described by nominal or dichotomous values were not entered into the factor problems. 
All of the variables in the target acquisition set loaded on a single factor. Tlus was categorized as com- 
prising the 'performance' attribute. The gun variable 'dispersion' is inversely related to accuracy. To 
channel the scoring thrust in a positive direction, this variable was transformed into a reciprocal. Two 
factors were extracted, with muzzle velocity and rate of fire loading heavily on one; and calibre, maximum 
effective range, and the reciprocal of dispersion loading on the other. The two factors were separated and 
scored as for airframes. In the air- to-air missile set, variables loaded on two factors. The first showed 
heavy loading for those variables related to a missile's performance or lethality (the six range related vari- 
ables, speed, warhead weight), while the second was composed of those defining a missile's vulnerability 
to detection and target maneuvering (diameter, weight, and a negative loading for the maneuverability 
variable, G limit). Since the maximum and minimum range variables against high and low altitude targets 
had been the values in the maximum effective range computations, they were set aside. The G limit vari- 
able was transformed into a reciprocal, so that highly maneuverable missiles would score lowest on the 
vulnerability attribute. Two separate factor scoring problems were formulated to derive scores for each 
attribute. 



ttttttttttttttttttttt 

Although the fuel fraction variable did not load on this factor, it was tested along with the range 
variables in deriving factor scores. Its inclusion generated results which in some instances were at 
drastic variance with known relative endurance qualities. The probable reason is that the variable 
accounts only for relative fuel capacity and not fuel consumption efficiency. It is likely a valid rela- 
tive indicator if a single class of similarly engined aircraft is under study. When applied across a 
sample as broad as this, its effects are counterproductive. 

- 79- 



5.3.2 Attribute Indices Utilization 

5.3.2.1 The Dilemma 

As noted previously, the aggregation methodology contemplated for this study demands attribute values 
be measured on ratio scales. The influence exerted by negative factor score coefficients was preempted by 
the insertion into each attribute problem of only those variables which load heavily (statistically and func- 
tionally) on the factor and the conversion to reciprocal values of those variables which load negatively. 
Still, the fact that all raw data are transformed into standardized values prior to score calculation stands as 

a barrier. Several mathmatical solutions were attempted, all basically anchored by tried techniques for 

12 
reversing the standardized scoring process. In fact, an arbitrary system was employed in the analysis 

prototype. The data bases all contained systems the performance characteristics of which verged on the 

minimum essential to a weapon which would have even a negligible combat impact. A nominal zero 

surrogate factor score was created at a point one standard deviation below the lowest authentic factor 

score in each attribute set. Its inverse was then added to each score on the attribute. The solution is 

workable but unsatisfying, smacking of smoke and mirrors. 

5.3.2.2 A Possible Resolution 

The threads of a possible solution reside in the nature of the data processed in this particular string of 
analyses. Since nominal and dichotomous variables were excluded from factor scoring, values for all 
remaining variables could be assumed to have ratio properties, including a natural zero point. It was 
observed that the few older aircraft which had no capacity to carry external ordnance (weapons stations 
and maximum ordnance = 0) still received a factor score value. Since the values for these cases consti- 
tuted valid natural zero points when entered into the problem, would not the scores generated for them 
also constitute the zero point of the factor score scale? 

To explore the potential, a 'control' case was created for each subsystem with a value of zero 
assigned to all its variables. Factor analysis was accomplished at the subcomponent level to determine if 
the insertion of the control case forced a redefinition of the factors (attributes). The basic groupings 
remained the same. The same procedure was employed for each attribute, this time with factor scores 
produced. The inverses of the values for the control cases were added to factor scores for the operative 
cases, creating sets of attribute values which intuitively had ratio properties. However, logical assertion 

does not leeitimate the approach. A more substantial token of validity is required, 
tttttttttfttttttttttt 



12 
13 



The AAC study, for instance, speculated that a value five standard deviations from the mean might 
constitute a reasonable surrogate for zero. 

As ludicrous as the example might seem, a notional aircraft with an absolute capability of zero would 
not fly. Thus, its airspeed, maneuverability, mission endurance, etc. would be zero. Despite the 
awkwardness of the conception, it is no more unrealistic to postulate than the notion of zero temp- 
erature or distance. 

- 80- 



5.3.2.3 The Ratio Test 

The key element in establishing credibility is to demonstrate that the adjusted scores possess the same 
ratio relationships as the input values. Reaching that goal with the study data files is patently infeasible. 
A notional three variable data set (VAR1, VAR2, VAR3) was created with values for ten cases. It is 
shown in Table 5.3. 'CaseO' was assigned values of zero for each variable, and 'Casel' was assigned the 
value of a prime number. Subsequent cases were given a value which doubled that for the previous case. 
The data were subjected to principal components factor analysis. All showed a loading of one on a single 
factor, with factor score coefficients of 0.33333. 





Table 5.3: An Observable Data Set 




CASE 


VAR1 


VAR2 


VAR3 


CaseO 











Casel 


1 


3 


5 


Case2 


2 


6 


10 


Case3 


4 


12 


20 


Case4 


8 


24 


40 


Case5 


16 


48 


80 


Case6 


32 


96 


160 


Case7 


64 


192 


320 


Case8 


128 


384 


640 


Case9 


256 


768 


1280 



The scores are listed under the heading FACTOR SCORES (RAW)' in Table 5.4. The inverse of 
the raw factor score for 'CaseO' (.61933) was added to the factor score for each case, and the results tabu- 
lated under the column annotated FACTOR SCORES (ADJUSTED)'. As can be readily seen, their 
values, with rounding, follow precisely the same progression as the input data. 



Table 5.4: 


Adjusted Ratio Level Scores 


CASE 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


CaseO 


-. 61933 


. 00000 


Casel 


-. 60721 


. 01212 


Case2 


-. 59509 


. 02424 


Case3 


-. 57085 


. 04848 


Case4 


-. 52237 


. 09696 


Case5 


-.42541 


. 19392 


Case6 


-. 23149 


. 38784 


Case7 


. 15635 


. 77568 


Case8 


. 93202 


1. 55135 



- 81 



5.3.2.4 The Distortion Test 

No solution is without its price, and the application of the zero based scoring technique appears to exact 
two. The first is the most troublesome. The inclusion of a control case unarguably alters the spread of 
the study data sets. As noted above, the factor patterns and score coefficients did not change, but a 
cursory review of scores for airframes with and without the control case showed the changes in the values 
of the derived factor scores for the active cases. 

The magnitude and direction of the changes had to be determined along with their effect on relative 
rankings. Factor scores were generated for five of the attribute groupings of the airframe data set under 
two conditions, one with the control case and one without. Ordinal rankings were determined for each 
attribute pair, and the results compared using a non-parametric correlation procedure. The results are 
depicted in Table 5.5. Clearly, the effects of the insertion of the control case on relative case rankings was 
negligible. 



Table 5.5: Impact of the Control Case on Rankings 

ATTRIBUTE SPEARMAN' s 

RHO 

Speed/Energy 0. 9997 

Maneuverability 0. 9999 

Air- to-Ground Range 0.9988 

Air-to-Air Range 0. 9906 

External Ordnance 0.9991 



To put the effects of the insertion of the control case in perspective, the same test was conducted, 
this time removing two active cases from the file (a fighter-interceptor and a ground attack fighter). The 
effect on the speed, maneuverability, and air-to-air range scores was comparable. However, the correla- 
tion of scores for the air-to-ground range and external ordnance attributes dropped to .9709 and .9566 

respectively. Thus, it can be safely assumed that the insertion of the control case has at least no greater 
ttftttttttttttttttttt 

Ironically, the inclusion of zero values forced a more normal distribution for several variables which 
were skewed to the right. 

All factor scores represent relative values within the confines of the factor space. Hence, the addition 
or deletion of any case, active or control, will change the relative scores and may change the relative 
rankings. These changes are a result of the standardization transformation which is applied to all 
absolute values prior to score generation. 



16 



Case by case results were also reviewed. The vast majority of rankings remained the same. Only a 
handful changed bv more than two positions and just one "by more than two positions (four). With 
the exception of the inexplicable four position change on one case, most ot the changes could be 
traced to order reversals among variants of the same basic airframe (i.e., MiG-25R and MiG-25U, 
Mirage-FIA and Mirage-FIB). While the reason for this phenomena is unclear, its effect is inconse- 
quential. 

- 82- 



effect on relative case rankings than would the addition or deletion of active cases. 

Although the effect of the control case on the scores' rank orders was inconsequential, it is prudent 
to observe its impact on the score values themselves. The same paired lists of scores were compared 
through the Wicoxon Signed- Pairs Test to determine the direction and locus of differences. Output sta- 
tistics reflect the same tendencies for each pair of lists. The means of the values falling in the first two 
quartiles were higher (less low) for the factor scores computed using the data sets including the control 
case. The reverse was true for values which fell above the median. The means, standard deviations, and 
and value ranges decreased slightly for the lists computed with the zero base. For each pair, the number 
of cases in which the zero based score increased was larger than the number in which the reverse was true. 
Within the more compact value ranges, scores toward the higher end of the scale increased slightly while 
those toward the bottom decreased, providing greater differentiation. Predictably, the two-tailed signifi- 
cance tests rejected the hypothesis that respective distributions were not similar (P = .0000). Coupled 
with the results of the rank order correlation test, these statistics suggest that the insertion of the control 
case does not adversely distort the sets of attribute factor scores. Conversely, an argument could be made 
that the zero values provided a more well-defined representation of the actual ratio differences among the 
active case input values, although this would be difficult to substantiate. 

5.3.2.5 The Scale Test 

The second price exacted by the adjusted scoring technique concerns the comparability of inter-attribute 
measurement scales. The raw scores for the zero point varied considerably among the attribute sets, 
ranging from a low of -1.90708 for the ordnance attribute to a high of -4.85510 for maneuverability. 
Thus, their inverses constitute an uneven threshold. The threshold values themselves would in effect 
determine a portion of the relative weight accorded each attribute during the additive phase of the scoring 
process, mirroring the problem caused by adding disparately scaled values discussed at the beginning of 
the chapter. After several false starts involving the computation of a grand mean across the attribute data 
sets, a variation on the indexing technique was adopted. The concept of indexing each attribute to the 
values for a given system satisfies the objective within the subsystem groupings, but fails to provide the 
desired common frame of reference across subsystems. A more viable alternative is to index each attribute 

score set to its own means. Considering the nature of the adjustment process, the mean of each score set 

I 8 
is equal to the inverse of the raw factor score of the set's control case. To cast the adjustment process 

t + ttttttttttttttttttt 

17 

Changes in case composition are made regularly. The initial airirame file, for instance, grew from 86 
to 125 cases over the course of the study. Since any list of cases represents a sample oi a larger uni- 
verse, the etfect of the inclusion or exclusion of cases does not constitute a invalidating factor. It 
merely expands or contracts the space within which relative values are determined. 



18 



Since the raw factor scores are standardized, their mean is 0. Adding the inverse of the raw factor 
score for absolute to the mean case creates a mean equal to the value of the inverse. 

- 83- 



in equation form, the adjusted factor score for Casel would be calculated: 

f la = ((f l + (% *-D)/(% *-l). where: I 

fi = Adjusted Factor Score for Casel 
f i = Raw Factor Score for Case 1 
fn = Raw Factor Score for CaseO. 

5.3.3 A Reduction Method 

The path might have been tortuous and its end, like that of any data reduction scheme, a less accurate 
portrayal of reality than its contributing parts, but a modestly geared factor analysis technique has suffi- 
cient merit on balance to warrant its employment. Of the alternatives, it best satisfies the four criteria for 
effective data reduction postulated in the introduction. Applied at the subsystem level in conjunction with 
subjective appraisal, it defines the groupings of variables which most efficiently captured an attribute's 
value. At the attribute level, it generates raw factor scores which portray the relative value of each case on 
a given attribute. Finally, the ratio properties of case scores can be restored in relation to a control case, 
and the adjusted scores indexed to their means to create a common frame of reference across attributes 
and subsystems. The outputs from this chain of analyses form the inputs along with the values for the 
nominally scored variables and relational variables to formulae computing a weapon system's relative 
technical potential in combat roles. These, in turn, can be mated with with force propagation attributes 
to determine aggregate potential at the national level. 

5.4 Data Reduction Results 

The spadework done, it remains to generate adjusted factor scores for the various subsystem attributes and 
judge the results subjectively. This section will touch on the salient points associated with each data 
reduction iteration, capsulize results, and offer some subjective assessments of them. Complete listings of 
the adjusted factor scores for each subsystem are presented in Appendix E. 

5.4.1 The Airframe Subsystem 

Scores for the five attributes comprising the airframe subsystem were derived using the minimalist factor 
analysis technique described in the preceding section. The raw and adjusted factor scores for the top 15 
scoring airframes are displayed in the tables for each attribute. Some cautionary notes are in order 
regarding interpretation of the data in the tables. Most importantly, the scores have been adjusted math- 
matically, but no modification has yet been made to account for the influence of nominally scored char- 
acteristics such as variable camber wings (maneuverability) or navigational capability (range). The per- 
ceptive reviewer will also note that, in some instances, airframes with slightly different raw factor scores 



84 



are shown as having the same adjusted factor score in the display tables. This anomaly is caused by the 
truncation for display purposes of the latter value to three decimal places. The automated files retain five 
decimal place values, which are used in aggregate score computations. The question may also arise as to 
why similar variants of an airframe have different scores on the same attribute, in particular maneuver- 
ability and detectability. It should be remembered that each variant is specifically configured, and its 
combat weight calculated on the basis of that configuration. Thus, the Tigershark variant whose radar has 
the continuous wave target illumination option installed (F-20A) is configured with AIM-7 and AIM-9 
air-to-air missiles, while the other variant (F-20) carries only the lighter AIM-9's. Since combat weight or 

a composite variable of which it is a component is involved in the factor analysis of these two attributes, 

19 
the scores can be dissimilar and legitimately so. 

5.4.1.1 Speed/Energy Attribute 

The raw and adjusted factor scores for fifteen airframes which scored highest in the 125 airframe set are 
depicted in Table 5.6. The location of the Mirage-FIE at the top of the list might seem surprising. 
However, the most capable configuration of this aircraft has modifications to cockpit transparency and 
wing leading edges which give it a Mach 2.5 capability at altitude, while retaining a Mach 1.2 top speed at 
sea level. Like all of the later model Dassault fighters, it also has a high rate of climb. The placement of 
the MiG-25R, which set high altitude speed records, in sixth position might also take some reviewers 
aback. But the MiG-25's have a relatively poor speed capability at lower altitudes due to their airframe 
design and structural composition. In fact, the positioning of the MiG-25's is an endorsement of the 
principal that a single dimensioned 'marker' variable is insufficient to portray a meaningful picture of 
combat speed. Finally, it is instructive to note that 1 1 of the 15 aircraft which rank highest on the speed/ 
energy attribute are not of U.S. or U.K. design. It has been observed that designers from these two 
countries have recognized the limited applicability of speeds in excess of Mach 1.8 in most combat sce- 
narios and have subordinated technologically attainable maximum speeds to other considerations such as 

20 
maneuverability. . 



ttttttttttttttttttttt 

Where multiple variants of a basic airframe have the same score on an attribute, the score is credited 
to a single designator describing all the variants to which the score applies (i.e., I' I5/A/B/C/D). 

See Gunston, Modern Air Combat pp. 14- 17, and pp. 186- 193, for an informative discussion ot the 
relative merits of various airframe attributes in combat. 

- 85- 



Table 5.6: 


Airspeed/ Energy 


i 
Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


MIRF1E 


1. 71643 


1. 734 


MIG29 


1. 36185 


1. 582 


MIG31 


1. 32272 


1. 566 


MIR2000C/T 


1. 31800 


1. 563 


MIG25R 


1. 29513 


1. 081 


MIG25/U 
MIR2000R 


1.21650 


1.520 


1. 19940 


1. 513 


F15A/B/C/D 


1. 18451 


1.506 


SU27 


1. 12331 


1.480 


MIG23G 


1. 09501 


1.468 


F15E 


1. 09396 


1.468 


MIR4000 


1. 09134 


1.467 


FA18L 


1.08935 


1.466 


F16A/B/C/D 
MIG23B 


1. 07952 


1.462 


1.07450 


1.459 






5.4.1.2 Maneuverability Attribute 

The factor scores scaling relative maneuverability, Table 5.7, will perhaps provoke the most controversy, 
since the results seem to challenge the assumed ascendancy of the lightweight fighter in this attribute. 
However, it must be remembered that the attribute adresses maneuverability in two dimensions, energy 
maneuverability or acceleration and instantaneous turning performance. The former dimension contrib- 
utes to the positioning of the F-15E and SU-27 at the top of the list. It also bears mentioning that the 
performance data on these fighters and on the MiG-29, Mirage-4000, and other new models are predicated 
on design goals or prototype test results and not on operational performance. It can be safely assumed 
that many of the values on yet-to-be-fielded systems will be altered when they reach operational status 
and track records are scrutinized. The high maneuverability rating of the planned export version of the 
Harrier (HARMK80) is consonant with its high thrust-to-weight ratio. In a continuation of a previous 
comment, note that 12 of the top 15 scores are awarded to fighters of American or British design. The 
maneuverability values shown will be further modified during the scoring procedure when the effect of 
devices which vary their wing camber is considered. 



- 86 



Table 5.7: 


Maneuverability Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


F15E 


2. 32053 


1.468 


SU27 


1. 87997 


1. 389 


F16A 


1. 86495 


1. 386 


F16B 


1. 85503 


1. 384 


F15C 


1. 83723 


1. 380 


F15D 


1. 78677 


1. 370 


MIG29 


1. 74691 


1. 361 


F20 


1. 72460 


1. 357 


F20A 


1. 61733 


1. 335 


MIR4000 


1. 61681 


1. 335 


F16CSC 


1. 57651 


1. 326 


F15CFP 


1. 55900 


1. 323 


F16C 


1. 51086 


1. 313 


HARMK80 


1. 50160 


1. 311 


F16D 


1.43996 


1.298 



5.4.1.3 Air-to- Air Range Attribute 

The highest relative air-to-air range or endurance scores for interceptors and multi-role fighters are listed in 
Table 5.8. The F-15CFP is an F-15C configured with conformal fuel tanks (FAST packs), which increase 
its sub-sonic area intercept and ferry ranges considerably. While ferry range has no intrinsic combat qual- 
ity, it suggests an airframe's endurance enhancement potential if external fuel tanks and fuel efficiencies are 
employed. Only two of the newest Soviet fighters appear near the top of this group which is dominated 
by Western produced airframes. 



ttttttttttttttttttttt 

21 i • 

This association is arguable. But a high fuel, light weapons load option would be called tor in some 

Mideastern combat scenarios where endurance is a primary concern. Iranian F-14s were reportedly 

employed in this configuration in the early stages of the war with Iraq. Thus, some measure of 

endurance expandibility potential was believed important enough to include. The same logic was 

used in deriving the air-to-ground factor scores. 

- 87- 



Table 5.9: 


Air-to-Air Range 


Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


F15CFP 


2. 35225 


1. 717 


F15E 


1. 78011 


1. 542 


F14AC 


1. 84757 


1. 563 


MIR4000 


1. 70393 


1. 519 


F15C 


1. 52780 


1.466 


F15D 


1. 34757 


1. 411 


TORADV 


1.27140 


1. 387 


MIR3NG 


1. 17925 


1. 359 


F15A 


1. 17463 


1. 358 


MIRF1E 


1. 03482 


1. 315 


F15B 


. 99440 


1. 303 


FA18L 


. 99292 


1. 303 


SU27 


. 95923 


1. 292 


MIRIIIE 


. 95836 


1.292 


MIG31 


. 86412 


1. 263 



5.4.1.4 Air-to-G round Range Attribute 

The top two positions in the air-to-ground range attribute list, Table 5.10, went to the two Soviet built 
bombers deployed in Middle Eastern countries. The inclusion of the earlier model F- 15 variants in this 
attribute group could be challenged. However, they do have a secondary attack capability if appropriately 
configured. In fact, some reports claimed Israeli Air Force F-15s participated in the bombing of the Osi- 
raq nuclear reactor. The extraction of scores in a secondary role on this attribute acknowledges the 
potential while offering no suggestion of its attainment. The air-to-ground potential scoring logic will 

consider the mission of the unit of assignment and the configuration of the air weapon system before ren- 

22 
dering a score at the force level. The Tornado Interdiction Variant (TORIDS) recently ordered by 

Saudi Arabia scored well on this attribute, as did several of the older single purpose ground attack fighters 

(A-7E, A-7P, Mirage-5D2, and A-4H). The air-to-ground range scores will be given the added dimension 

of 'effective' range, when modified by navigation capability values in the scoring process. 



ttttttttttttttttttttt 

22 

Saudi Arabian F-15s are not equipped for air-to-ground missions, nor are their aircrews trained in 

them. 

- 88- 



Table 5.1 1: 


Air-to-Ground Range 


Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


TU22BD 


4. 74706 


2. 924 


TU16AG 


4.29450 


2. 740 


F15CFP 


2. 04871 


1. 830 


F15E 


1.49469 


1. 606 


F15C 


1. 39035 


1. 563 


TORIDS 


1. 36715 


1. 554 


A7E/P 
MIR5D2 


1. 32630 


1.537 


1. 28591 


1.521 


F15D 


1. 25992 


1. 511 


MIR3NG 


1. 19300 


1.483 


A4H 


1. 19024 


1.482 


IL28 


1. 10718 


1.449 


F15A 


1. 03215 


1.418 


FA18L 


. 93925 


1. 381 


MIR4000 


. 62108 


1.252 



5.4.1.5 Air-to-Ground Ordnance Attribute 

The air-to-ground ordnance attribute scoring problem considered two aspects: the maximum ordnance 
weight which could be carried and the number of positions on which it could be carried. The results for 
the top 15 scoring airframes are included in Table 5.12. The number of stations was included in the factor 
problem to capture the flexibility in ordnance mix engendered by multiple stations. The large number of 
weapons positions available propelled the A-10A over seven other systems which have a greater total car- 
rying capacity. While this result might raise eyebrows, the facet of multiple weapons type capability 
which it portrays is important. The F-4MOD in the third position is a 'paper airplane' at present, a 
design proposal developed by the Boeing Corporation and the Israeli Air Force to modify a portion of the 
IAF's F-4s drastically to increase range and carrying capacity. Note the presence of just two Soviet fight- 
ers in the top grouping, the SU-25 and SU-22 ground attack aircraft. Soviet fighters generally scored low 
on this attribute and on the air-to-surface range attribute, indicative of the relatively weak air-to-ground 
potential of aircraft supplied Middle Eastern clients by Moscow. During score computation, the adjusted 
scores will be further differentiated to account for the precision and non-precision ordance delivery capa- 
bilities of the host aircraft. 



ttttttttttttttttttttt 

yx ...... 

An alternative scoring process was also tried for this attribute, simply indexing maximum external 

ordnance to the mean of the of the variable set. The results shifted some individual scores, but the 

rank order correlation remained relatively high (r = .7917). The indexed scores were retained for 

further sensitivity analysis in the combat potential computation phase. 

- 89 - 



Table 5.12: Air-to-Ground Ordnance Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


TU22BD 


2. 96083 


2. 342 


F15E 


2.43530 


2. 095 


F4M0D 


2. 39578 


2. 077 


TORIDS 


2. 09097 


1. 935 


MIR4000 


1. 88353 


1. 838 


TU16AG 


1.83921 


1.814 


A10A 


1. 82164 


1.813 


FA18L 


1. 57579 


1. 692 


SU25 


1. 30224 


1. 571 


F4EF 


1. 27546 


1. 546 


F15CFP 


1. 23371 


1.530 


F16A/B/C/D 


1. 15844 


1.495 


LAVI 


1. 03694 


1. 448 


MIR2000C/T 


1. 03520 


1.437 


SU22 


1. 01349 


1.431 






5.4.1.6 Detectability Attribute 

The final table, Table 5.13, lists the results of the vulnerability to detection segment of the factor scoring 
process. Unlike the preceding tables, Table 5.13 depicts the 15 airframes with the lowest scores, the ones 
least likely to be detected based on their size and combat configuration. The factor scores will be one of 
four elements of the vulnerability to engagement compuation. The others are speed, maneuverability, and 
electronic combat capability. 



90 



Table 5.13: 


Airframe Detectability 


Factor Scores 


AIRFRAME 


FACTOR 


FACTOR 




SCORE 
(RAW) 


SCORE 




(ADJUSTED) 


SF260TP 


-1. 00573 


.499 


SF260MW 


-1. 00279 


. 500 


F5A 


-.81966 


. 591 


F5B 


-. 81507 


. 594 


F5E 


-. 71492 


. 644 


F5F 


-. 69438 


. 654 


RF5E 


-. 69432 


. 654 


F104GCF 


-. 67349 


. 664 


F20 


-. 66937 


. 666 


F20A 


-. 65667 


. 673 


HARMK80 


-. 65291 


. 675 


MIG21F 


-. 64302 


. 680 


MIG21C 


-. 64136 


. 680 


PRCF7 


-. 64136 


. 680 


MIG21JKL 


-. 63966 


.681 



5.4.2 Target Acquisition Systems 

As noted previously, all of the ratio level variables which described a target tacquisition system's detection 
potential loaded positively on the same factor. The results of the factor scoring process for the ten highest 
scoring systems, all multi-mode or air intercept radars, are depicted in Table 5.14. The large and powerful 
AN/AWG9, which is fitted to the F-14A/C topped the list, followed by the very capable Marconi/ Ferranti 
FOXHUNTER air intercept radar carried by the Air Defense Variant of the Tornado. The AN/APG70 
is a multi-mode system which will be installed in the F-15E, while the AN/APG63 and AN/APG64 are 
associated with operational variants of the F- 15. The AN/APG67 is the multi-mode radar General Elec- 
trics produced for the F-20A, and the AN/APG68 is the up-graded system installed in the latest F-16's. 
The 'FLANRAD' and 'HOUNDRAD' are the radars installed in the two newest Soviet interceptors, the 
SU-27/Flanker and MiG-31/Foxhound respectively. Their performance characteristics have been esti- 
mated. The RDM is a multi-mode radar produced by Thompson-CSF for installation in export versions 
of the Mirage 2000 series. The detection values for the target acquisition effectiveness attribute will 
change somewhat when they are combined with nominally described characteristics (electronic counter- 
counter measures, track while scan, and doppler beam sharpening) in the combat potential computations. 



- 91 - 



Table 5.14: 


Target Acquisition System Factor Scores 


SYSTEM 


FACTOR 


FACTOR 




SCORE 
(RAW) 


SCORE 




(ADJUSTED) 


AWG9 


2.24577 


2. 189 


FOXHUNT 


1. 96710 


2. 042 


APG70 


1. 96316 


2. 039 


APG64 


1. 92754 


2. 021 


FLANRAD 


1. 85371 


1. 982 


HOUNDRAD 1.75172 


1. 928 


APG63 


1. 66166 


1. 880 


APG67 


. 90713 


1.480 


APG68 


.84123 


1.445 


RDM 


. 71547 


1. 379 



5.4.3 Air-to-Air Missile Subsystems 

In no aircraft subsystem are the tradeoffs between performance and vulnerability to detection and defeat as 
evident as in the air-to-air missile category. The size required to house a more sophisticated radar based 
guidance system, a larger warhead, and sufficient propellant to generate longer ranges increases the poten- 
tial that the missile will be detected and outmaneuvered. Relative lethality scores are displayed in 
Table 5.15. All the missiles placing in the top ten depend on radar guidance. All but two, AIM-54 
(PHOENIX) and AIM-120A (AAMRAM), have semi-active radar homing (SARII) terminal guidance 
systems, forcing the launching aircraft's radar to continue target illumination until impact. This factor, 
which increases the launch aircraft's own vulnerability, will be considered in the combat potential compu- 
tation. 

Several of the missiles which gained the highest lethality scores are also the ones most susceptible to 
detection and defeat, as demonstrated in Table 5.16. While the top of the list is occupied by an older 

missile not among the top performers, the Soviet AA-6 (ACRID), the remaining entries correspond to six 

25 
of the missiles which ranked highest in performance. The western edge in micro-electronics can be 

assumed to have contributed to absence of AAMRAM and the newest French radar guided missile (Super 
530 D) from the top of the vulnerability list. The vulnerability scores will be further adjusted to account 
for the guidance system's resistance to electronic counter-measures and will denominate the overall com- 
bat potential score. 

tttfttttttttttttttttt 

Gunston points out, for instance, that a pilot who has detected a Mach 3 air-to-air missile with a 
30G turning limit can outmaneuver it by making a 3G turn at 450 knots. See Modern Air Combat, 
p. 15. 

The 'B' model designator on Soviet missiles is assigned to those variants of the basic missile which 
have infra-red terminal guidance. The weights vary slightly between the guidance systems; thus, the 
differing vulnerability scores. 

-92- 



25 



Table 5.15: Air- to- Air Missile Performance Factor Scores 


MISSILE FACTOR 


FACTOR 


SCORE 
(RAW) 


SCORE 


(ADJUSTED) 


AIM54 3.88712 


3.206 


AIM7F/M 1.65487 
SUP530D 1.26857 


1. 939 


1. 720 


AA9A 1.20678 


1. 685 


ASPIDE . 84823 


1.483 


AIM7E . 84823 


1. 362 


SUP530F .62061 


1. 352 


AIM120A .61216 


1. 347 


AA7A .58902 


1. 334 


SKYFLASH .52188 


1.296 



Table 5.16: Air-to 


-Air Missile Vulnerability Factor Scores 


MISSILE 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


AA6A/B 
AIM54 


2.80864 


2.210 


1. 75773 


1. 757 


AA7A 


1. 13195 


1.488 


AA7B 


1.08042 


1.466 


AA9A 


1.06897 


1.461 


ASPIDE 


. 78797 


1. 340 


SKYFLASH 


.73226 


1. 316 


AIM7D 


. 63438 


1.273 


AIM7C 


. 56567 


1.244 


SUP530F 


.53167 


1.210 



5.4.4 Aerial Gun Subsystems 

The assignment of meaningful descriptive titles to the two factors associated with aerial guns was not 
clearcut. Rate of fire and muzzle velocity loaded heavily on the first factor, while the other variables 
loaded moderately, with the exception of calibre, which loaded negatively. The second factor showed 
heavy loadings for calibre, maximum effective range, and accuracy. The identifications of the two group- 
ings (rate of fire and effectiveness) are subjective approximations of the attributes they represent. The top 
ten scores for each attribute are listed in Table 5.17 and Table 5.18 respectively. The patterns depicted 
reflect reasonable relationships among the relative overall effectiveness of the weapons. 
The two factor scores will be combined according to their relative contribution to overall performance 
variance in developing a single measure of gun effectiveness. When mated to an airframe, their effcctive- 



93 - 



Table 5.17: 


Aerial Gun Rate of Fire 


Factor Scores 


GUN 


FACTOR 


FACTOR 




SCORE 
(RAW) 


SCORE 




(ADJUSTED) 


GAU12U 


1.58126 


1. 646 


GAU8A 


1. 51434 


1. 619 


MKIIM0D5 


1.43403 


1. 586 


M61A1 


1.43403 


1. 586 


NR30GAT 


1. 34511 


1. 556 


XM27E1 


1. 00225 


1.410 


M39 


. 98490 


1.403 


GAU2BA 


. 90187 


1. 369 


M28 


. 90187 


1. 369 


GAU13A 


. 75365 


1. 308 



ness will be further differentiated by the host's ordnance carrying capacity (rounds) in developing a net 
gun potential value. Several of the guns in the analysis are mounted in external pods. These are not 
mated to aircraft in the present configuration file, but scores were generated for them so that they could be 
considered as armament options in later analyses if desired. 



Table 5.18: 


Aerial Gun Effectiveness Factor Scores 


GUN 


FACTOR 


FACTOR 




SCORE 


SCORE 




(RAW) 


(ADJUSTED) 


GAU13A 


1. 68924 


1. 573 


GPU5A 


1.44211 


1.489 


DEFA554 


1.44211 


1.489 


MAU27 


1. 30054 


1.441 


KCA30 


1. 19218 


1.405 


XM8 


1. 10246 


1. 374 


DEFA553 


. 97522 


1. 331 


M621 


. 73419 


1. 249 


M5 


. 63167 


1. 214 


GAU8A 


. 63055 


1.214 



5.4.5 Maintenance Force Quality 

As remarked earlier, the use of national scores to quantify relative measures of the quality of maintenance 
forces is an illustrative sidebar to this study. Nevertheless, the process through which the relative values 
were derived deserves brief mention. The four variables standing in for motivation (armed forces per 
thousand, military expenditures per capita, military expenditures as a percentage of GNP and as a per- 



-94- 



centage of central government expeditures) and the two suggesting technical capacity (literacy rate and 
percentage of eligibles in secondary school) were introduced into a factor problem. A notional country 
with zero values was added to the 22 active cases, and scores extracted. Although two factors emerged 
under rotation, all variables loaded significantly (at least 0.6) and positively on the first one. It was 
selected as being sufficiently representative. The raw and adjusted factor scores for all 22 countries are 
listed in Table 5.19. Adjustments to this data set were made in a slightly different fashion than for weap- 
on systems. It was assumed that the the qualitatively most proficient maintenance personnel would gen- 
erate one perfect maintenance manhour. Relying on historical observations, the quality of Israeli mainte- 
nance manpower was assigned a value of one, and all other observations were scaled to it in proportion to 
their raw factor scores. 



Table 5.19: Maintenance 


Manpower Quality 


Factor Scores 


COUNTRY 


FACTOR 


FACTOR 




SCORE 
(RAW) 


SCORE 




(ADJUSTED) 


Israel 


2. 37109 


1. 000 


Jordan 


1.45151 


. 790 


UAE 


1.00045 


. 688 


Iraq 


.97870 


. 683 


Oman 


. 75180 


. 631 


Syria 


. 61468 


. 600 


Qatar 


. 61238 


. 599 


Libya 


.46904 


. 567 


Saudi Arabia 


.44771 


. 562 


Kuwait 


.42115 


. 556 


Egypt 
Lebanon 


. 13173 


.490 


-. 08363 


.441 


Iran 


-. 15596 


.424 


PDRY 


-. 24876 


.403 


Bahrain 


-. 34915 


. 380 


Somalia 


-. 64010 


. 314 


YAR 


-. 82650 


. 271 


Tunisia 


-. 82826 


. 271 


Algeria 


-. 83542 


. 269 


Morocco 


-. 92810 


. 248 


Ethiopia 


-1.05612 


. 219 


Sudan 


-1.28085 


. 168 



While these data are patently superficial, the relative associations among the countries are generally 
congruent with other studies and subjective appraisals. They should be approached gingerly, recognizing 
the fact that the input data captured only a fragment of the societal and organizational complex wluch 
determines force quality. The quality of maintenance force indices will be used to modify the man main- 
tenance hours available data in the final step in the national air combat potential equations. 



- 95 



5.5 Summary 

Data were reduced to a manageable matrix through a system which capitalizes on the most attractive 
aspects of several different data reduction techniques. The resultant body of data represents the relative 
quantities of each attribute which a subsystem possesses with the loss of significant information minimized 
to the extent permitted by any reduction scheme. Variables not lending themselves to higher orders of 
measurement were not forced into statistical problems ill-suited to their evaluation. Most importantly, the 
temptation to substitute neat statistical formulations for weighting relationships better determined by 
expert operational judgment has been eschewed. Within the context of the study framework, the bulk of 
the information required to calculate estimates of national air combat potential is now in place. 









96- 



Chapter 6 
AIR COMBAT POTENTIAL SCORE COMPUTATION 

Having plowed through the variable selection, data collection, and data reduction processes, the final step, 
air combat potential score computation, is almost anti-climactic. The evolution of national force level 
scores follows the hierarchical path outlined in Chapter 3. Air weapons scores are first computed at the 
subsystem level. These scores are aggregated, in turn, at the air weapons system level in consonance with 
specified system configurations and relational utility values. The force propagation branch computations 
are less elaborate. Raw inventories must be transformed into operational mission specific force levels and 
potential sortie rates estimated. In the ultimate step, the two branches are joined to calculate the maxi- 
mum relative combat potential a national force could expect to achieve under optimum circumstances on 
a given day. The nuts and bolts of the scoring sequence are outlined in the following sections, addressing 
the air weapon system process first. 

6.1 Air Weapon Systems 

6.1.1 Principles 

Before dissecting the individual system scoring iterations, a few general comments are in order. The com- 
putational philosophy adopted in this phase is derived substantially from the TASCFORM ' method- 
ology. While the following aggregation formulae and input variables deviate in some significant aspects, 
the path cut by TASC offered the most thoughtful and comprehensive approach encountered. Some rel- 
evant assumptions undergird the specific procedures. 

First, air weapon subsystems and systems are treated as linear combinations of attributes and sub- 
systems respectively. The single exceptions are measures of vulnerability, which are used to depreciate the 
potential of the system as a whole. While the assumption of linearity sacrifices the dynamic of synergy 
among system parts, the latter proved impossible to capture in a broadly based aggregated model. 

Second, before subsystem scores are computed, the raw attribute values evolved in the data reduction 
phase are modified by nominal values for those characteristics which enhance or diminish their potential 
but which were not suitable candidates for factor scoring. Variables such as electronic combat suite and 
navigation capability are examples of modifying variables. Since all of the modifying variables were nom- 
inal, indicating the presence or absence of a combat related quality, the scoring strategy aimed at assigning 



97- 



them values which reflected their functional impact on the attribute being modified. For the most part, 
analogous values were extracted from the TASC study, recast to accommodate procedural differences, and 
submitted to a panel of fighter experts for review. Values were adjusted in accordance with the panel's 
recommendations. As with any modifying factor or utility value in the computation process, their values 
can be adjusted by users to accommodate differing perceptions or priorities. 

Finally, combat potential scores are computed as a function of the mission(s) in which the air weap- 
on system might conceivably be employed. Four mission areas are addressed: air defense, fighter or air 
superiority, interdiction, and close air support. For the purposes of this investigation, the air defense mis- 
sion includes point and barrier defensive counterair operations. The fighter mission represents over-the- 
battlefield air superiority and escort employments. Interdiction includes deep interdiction and offensive 
counterair operations, and the close air support mission area subsumes direct air support of ground forces, 
battlefield area interdiction, and counterinsurgency applications. Mission differentiation among the com- 
bat potential scores for a given system is a function of its configuration and the mission specific relative 
utilities extracted from the aircrew survey discussed in Chapter 4. As with the modifying variables, these 
utility values are user-adjustable during score computation. 

6.1.2 Airframes 

The relative potential of an airframe in a combat role (AF ) is a product of the attribute values for 
airspeed/energy (NFSS), maneuverability (NFSM), and range/ endurance (NFSR ) and their respective 
relative utility values (e.g., US for the relative utility of the airspeed/ energy attribute). The maneuver- 
ability attribute is modified by a factor (MA) which accounts for the influence of devices which vary wing 
camber, such as leading edge slats or maneuvering flaps, thus enhancing turning performance. The precise 
effect of such devices varies from airframe to airframe. In the absence of specific data, a general value of 
1.2 was selected as representing the best estimate across the field. Specific values can be substituted when 
known. The range/endurance value is modified by two factors, one of which is linked to aerial refueling 
capability (RA) and the other to navigation capability (NA ). Since aerial refueling is dependent on the 
availabilty of tankers, it will not be included in the baseline calculations. Its effects will be demonstrated 
in a country-specific example later. The navigation modifier aims to transform theoretical range into 
effective range by tapping the capability of an airframe to exploit its full range potential. An experienced 
navigator assigned relative values to navigation categories ranging from dead reackoning (.6) to global 
positioning system (1.4). These values were further differentiated according to the relative importance of 
navigation in each mission area. Scores for airframe potential are calculated: 
AF f =(NFSS*US r ) + (NFSM*MA*UM r )+ (NFSR r *RA*NA r *UR r ) 



98- 



To demonstrate the implementation of this equation, the following example is the computation of 
the combat potential score for the F-16C in the fighter mission role. The F-16 has leading edge flaps and 
trailing edge flaperons for increased maneuverability and is equipped with an inertial navigation system. 

AF f = (.30*1.462) + (.43*(1.2*1.312)) + (.27*(1.2*1.113)) 

AF f = 1.467 

6.1.3 Target Acquisition Systems 

The target acquisition computation assesses an aircraft's target acquisition systems' potential to detect, 
identify, and provide engagement related information concerning a target in various combat roles. Mis- 
sion and aircraft non-specific scores (NFSTA) were derived for individual subsytems in the data reduction 
phase. The air weapon system configuration hie mated subsystems to aircraft variants. As was the case 
with the airframe calculation, several of the initial subsystem attribute values are modified by nominally 
measured characteristics in the initial phase of the computation. Visual acquisition capability is enhanced 
by multiple aircrew members. Differing expert opinions were offered on the percentage improvement in 
visual acquisition afforded by a second set of eyes, noting that experience, workload, and personal quali- 
ties were key determinants. In the absence of a consensus, a factor (VA) of 1.3 was identified as an aver- 
age position. Radar scores did not consider nominally described variables such as the presence of track 
while scan, doppler beam sharpening, and target illumination capabilities or address a system's relative 
resistance to electronic counter measures. Presence of a track while scan capability was estimated to 
enhance target acquisition by 30 percent in the air-to-air roles, and doppler beam sharpening by 20 per- 
cent in the air-to-ground roles. The target illumination modifying value was set at 1.2 for laser systems 
which provided a self-designating capability. These values were combined for each system into a modify- 
ing variable (TAA ). Resistence to electonic countermeasures values (ECCM) ranged from 0.7 to 1.1. 
Values were awarded to systems based on descriptions of their frequency agility, side lobe suppression, 
and other features which diminish the effects of countermeasures. Utility values weight the subsystems' 
relative contributions to successful target acquisition in four combat roles. The target acquisition score 
(TA v calculation for an aircraft with visual (TAV), radar (TAR) and secondary subsystems (TAS) would 
take the following form: 

TAV = (NFSTA vis *VA*ECCM) 

TAR = (NFSTA rad *TAA*ECCM) 

TAS = (NFSTA sec *TAA + ECCM) 

TA r = (UTV r *TAV) + (UTR r *TAR) + (UTS r *TAS) 
Again, the F-16C in a fighter role is presented as an example. It is a single-seat fighter equipped in 
this configuration with an AN/APG68 multi-mode radar and a laser range finder. Since the laser range 



99 - 



finder has no application in a fighter role, the value for a secondary acquisition system is set to zero. The 
AN/APG68 has track-while-scan and doppler beam sharpening capabilities and has a relatively high 
degree of resistance to electronic countermeasures. Just the values in the final equation are depicted 
below. 

TAf= (.22*215) + (.51*2.290) + (.17*0) 

TAf= 1.256 

6.1.4 Weapons Pay load 

The calculation of weapons payload potential values (PL ) involves a number of steps and, unlike those 
for the previous subsystems, is applied in two different forms depending on mission category. The 
expression for aerial guns will be presented first, followed by discussions of air-to-air missiles and air-to- 
ground ordnance. 

6.1.4.1 Aerial Guns 

Aerial guns were scored on two attributes, the rapidity and velocity with which they could deliver ord- 
nance (NFSRAT) and its effectiveness (NFSEFF). A third factor associated with the host aircraft, the 
volume of ordnance available, must be entered into the equation. The total number of rounds carried by 
each aircraft was computed and indexed to the mean of the data set. The resulting variable (NRND) is 
used in the scoring process to modify the NFSRAT value. Since values for the relative utility of rate and 
volume of fire (URAT) and ordnance effectiveness (ULEF) had not been established via the aircrew sur- 
vey, they were assigned subjectively. The equation for the mission non-specific combat potential score for 
an aerial gun (PLG) is: 

PLG = (URAT+NFSRAT+NRND) + (ULEF+NFSEFF) 
When applied to the M61A1 carried by the F-16C, the associated values are: 

PLG = (.6+1.546+1.573) + (.4+1.073) 

PLG = 1.889 

6.1.4.2 Air-to- Air Missiles 

The data reduction process scored air-to-air missiles on two attributes, performance (NFSPERF) and 
vulnerability to detection and defeat (NFSVUL). Two descriptive variables, guidance system type 
(GLTDTYP) and susceptibility to electronic countermeasures (ECS) modify the respective attribute 
scores. The values associated with guidance type (GUDIDSC ) were assigned subjectively, considering 
such features as relative accuracy and the ability to track a target without continuing input from the 
launching aircraft. The values ranged from .7 for a command guided missile to 1.2 for one with its own 
active radar homing system. The modifying factors were further differentiated by their launch parameters 



100 



within or beyond visual range and the weight of that capability in air defense and fighter type engagements 
respectively. A weight of one was awarded an infra-red guided system in a fighter role at the low end of 
the spectrum, while a weight of 1.6 for an infra-red system with beyond visual range capability in the air 
defense role topped the list. The susceptibility to electronic warfare modifier was also constructed sub- 
jectively, relying largely on descriptive information. Missiles least vulnerable to electronic warfare (to 
include chaff and flares) were assigned a value of .8. Those with high susceptibility were assigned a value 
of 1.1. Combat potential scores (PLM ) were computed for missiles in each of the air-to-air roles 
according to the following equation: 

PLM r = (NFSPERF*GUIDSC r )/(NFSVUL*ECS) 
Note the use of the modified vulnerability value as a denominator. This combinational technique 
acknowledges that a system's vulnerability to defeat depreciates the value of its performance in full pro- 
portion. A sample computation is shown for the AIM-9L missile carried by many U.S and Western 
fighters and just recently exported to some Middle Eastern countries. 

PLM f = (.864+l)/(.643*.8) 

PLM f =1.680 

6.1.4.3 Air-to-G round Ordnance 

A single air-to-ground ordnance attribute score (NFSO) was extracted during data reduction, but greater 
differentiation is needed to account for precision guided munitions capability (PGMC) and avionics sys- 
tems which enhance the accuracy of unguided ordnance delivery. Precision guided munitions are unar- 
guably more accurate than their unguided cousins, producing more effective 'bang' for the same ordnance 
load 'buck'. However, the extent to which accuracy is enhanced over that provided by a combination of 
freefall ordnance, modern release point computers, and head-up displays is the subject of considerable 
debate. Individual comparisons of specific weapons, delivery parameters, and target arrays can be com- 
puted using weaponeering algorithms. However, these are not suited to application in a study such as 
this. Consequently, modifying values were assigned in accordance with the following assumptions. A 
stability augmented (SA) aircraft with a modern release point computer (CRP) and a head-up display 
(HUD) can deliver freefall munitions at accuracies approaching those of all but the most advanced preci- 
sion guided systems. While precision guided munitions display greater accuracies, their effective employ- 
ment can be degraded by dust, haze and darkness and by their somewhat rigid delivery parameters. While 
their theoretical accuracies might eclipse those of freefall ordnance by a factor of four or higher, their 
practical combat accuracies are more modest. The accuracy value of freefall ordnance delivered by a sta- 
bilized platform equipped with a release point computer and a HUD was assigned a baseline accuracy 

TTT+ + JTT + TJ + + j + + -[- "fff •f 

No such svstem is currently operational, but the iogic was included in the scoring sequence to permit 
expandability. 

- 101 - 



value of one. The generic precision guided munition (OAPG) was assumed to be 40 percent more effec- 
tive on the average. A descending scale was used to score non-guided mumtions delivery accuracy 
(OANG) ranging from 1 for a full suite of delivery assistance equiptment to 0.2 for an aircraft with just an 
iron sight. The two following equations apply: 



PLO = (NFSO*OANG) 
PLO = (NFSO+OAPG) 






Substituting the values for the F-16C, which can deliver precision guided munitions and which is 
equipped with a CCIP/CCRP type weapons delivery computer and a HUD, the computations run: 

PLO ng = (1*1.495) 

PLO ng = 1.495 

PLO pg = (1.4*1.495) 
PLO pg = 2.093 

6.1.4.4 Full Payload 

Computing an aircraft's payload potential score (PL ) is a matter of combining invidual weapons type 
scores in accordance with information specified in the configuration file and weighting them according to 
relative utility values by mission (UIM , URM , UGU ). PL is computed separately for the air-to-air 
and air-to-ground missions. First in the air-to-air roles, the equation below applies: 

PL r = (UIM r *(NAAMI/2)*PLM r ) + (URM r +(NAAMR/2)*PLM f ) + (UGU/PLG) 
The number of missiles carried (NAAMI or NAAMR, infra-red and radar guided respectively) is divided 
by two to establish an indexed basic load. Earlier tests showed that, without this convention, the cumu- 
lative weight of multiple missile scores dominated subsequent air weapon system calculations. The F-16C 
is again used to demonstrate the computation. The latest version of the F-16C equipped with the 
AN/APG68 radar is reportedly capable of carrying radar guided (SARH) missiles. The following calcula- 
tion is based on a weapons suite of two AIM-7F's, two AIM-9L's, and an M61A1 aerial gun and 
addresses the fighter mission. 

PL f = (.39*(2/2)* 1.680) + (.39+(2/2)+2.067) + (.22*1.889) 

PL f = 1.877 
A similar set of equations determine payload potential scores in the air-to-ground missions. The rel- 
ative utility weights for guided and unguided munitions are UPG and UNG respectively. 

PL r = (UPG r *PLO ) + (UNG r *PLO )+ (UGU r *PLG) 
Substituting values and relative weights for the F-16C in an interdiction role, the equation would read: 

PL; = (.48*2.093) + (.38*1.495) + (.14*1.889) 

- 102- 



PL i = 1.837 

6.1.5 Vulnerability 

As noted earlier, vulnerability to engagement has two contrary dimensions, detectability and the ability to 
avoid engagement once detected. The first dimension is captured by the size attribute scored in the data 
reduction process (NFSV). The second is a product of an aircraft's speed (NFSS), maneuverability 
(NFSM), and electronic warfare capability (EC ). The first two avoidance attributes were determined 
previously. Electronic warfare capability is influenced by the ability to know that one has been detected 
(RWR) and to degrade the effectiveness of opposing target acquisition systems through passive (PECM) 
or active (AECM) means. These variables are nominally described, so the first task is to develop values 
which represent their influence in avoiding detection and engagement. The basic assumption governing 
the assignment of values was that possession of the full suite of electronic warfare capabilities applicable to 
a given mission would diminish an aircraft's vulnerability to the full value consistent with the relative util- 
ity of ECM in a combat role. Since the vulnerability equation is additive, an aircraft with a full comple- 
ment of ECM assets would have an EC score of zero. Weights for the relative utility of each system in 
varying roles were determined subjectively after discussion with fighter experts. EC values were comput- 
ed by the equation in which the presence of the characteristic is indicated by a 1 : 

EC r = l-((URWR r *RWR) + (UPCM r *PECM) + (UACM*AECM)) 
An aircraft with a full ECM suite would score 0; one with no ECM capability would score 1. 

With the establishment of the EC values, all the information required to formulate the vulnerability 
equation was at hand. The offsetting nature of the two families of attributes posed a combinational chal- 
lenge. Various strategies were tested before an approach which best portrayed the influence of the relevant 
attributes and was conducive to further applications was identified. Initial vulnerability to detection is 
largely a product of an aircraft's size. Speed, maneuverability, and electronic combat capability diminish 
that vulnerability somewhat, but their most significant contribution is in avoiding engagement once 
detected. The lower an aircraft's potential speed, maneuverability, or electronic combat capability, the 
higher the probability it will be engaged when detected. To preserve the additive combinational form, 
values for those attributes which diminish vulnerability first had to be transformed into reciprocals. The 
reciprocals were entered into the vulnerability equation in proportion to the relative utility values (UVS , 
UVM , UVE ) established by the survey and added to the value for detectability multiplied by its utility 
factor (UW) Thus, the vulnerability to engagement potential of a fast, maneuverable aircraft with a full 
electronic counter-measures suite would be largely limited to its detectability. In mathmatical form, 
potential for detection and engagement is calculated: 

V r = (UVY r *NSFV) + (USV r *( 1/NFSS)) + (UMV f *(l/NFSM)) + ( UEV r *EC f ) 
Substituting values for the F-16C in the fighter role, the computation reads: 

- 103 - 



V f = (.22*.900) + (.28*(l/1.462) + (.32*(l/1.312) + (.18*0) 

V f = 0.633 
Applying formula across the spectrum of aircraft and missions produced reasonable differentiation. 
The least vulnerable aircraft in the air defense and fighter roles scored as being approximately half as likely 
to be engaged as the most vulnerable aircraft accomplishing those missions. The range of values for the 
interdiction and close air support missions was considerably greater due to the inclusion of bombers and 
low performance aircraft in those mission areas. The ratios between most and least vulnerable aircraft in 
the air-to-ground categories were 3.5 and 5.3 respectively, not unrealistic considering the the low survival 
expectancy of an aircraft like an SF-260 in a moderately dense defensive environment. 

6.1.6 Combining Subsystems 

The final step in solving the air weapon system combat potential puzzle is to assemble the pieces accord- 
ing to their relative utlities in individual combat roles. No modifying factors are involved, so the procedure 
is considerably cleaner than those discussed above. Airframe, target acquisition, and payload values are 
multiplied by their relative utility values (UAF , UTA , UPL ) and added. The sum is depreciated by the 
value describing the aircraft's relative vulnerability to engagement. Mathmatically, the formula is: 

ACP r = ((UAF r *AF r ) + (UTA r *TA r ) + (UPL r *PL r ))/V r 
Substituting the values for the previously described F-16C equipped with two AIM-7F and two AIM-9L 
air-to-air missiles, air combat potential in the fighter role would be calculated: 

ACP f - ((.33* 1.476) + (.37* 1.256) + (.30* 1.877))/.633 

ACP f = 2.392 
Alternatively in the interdiction role, the F-16C's combat potential would be computed: 

ACPj = ((.27*1.329) + (.37+1.023) + (.36+ 1.837))/.589 

ACPj = 2.374 
Lacking a better term, the product of these equations will be referred to as 'Air Combat Potential 
Units' (ACPU's). It should be remembered that they represent the full theoretical combat potential of a 
specifically configured aircraft in a particular mission role relative to the potential of other aircraft in the 
data set in the same role. Thus, adding the ACPU's of a given aircraft does not produce a measure of 
total combat potential across a spectrum of missions. Altering aircraft configurations or changing the 
composition of the data set will yeild different ACPU values. The methodology was designed this way to 
permit evaluation of alternative configurations. Similarly, input relative utility values applicable to the 
entire mission set can be modified to accentuate a given attribute or subsystem corresponding to a specific 
employment environment or combat requirement. Again, the ACPU's generated will change. They are 
dynamic relative indicators not absolute measures of air weapon system worth. 



104 



6. 1 .7 Air Weapon System Results 

Illustrations of the output from the air weapons system assessment process are displayed in the next four 
tables, one for each mission area. Each table lists the 15 aircraft which scored highest in the category, 
along with their Air Combat Potential Unit (ACPU) values and the values for their subcomponents. As 
previously, multiple similarly configured variants have been compressed into a single entry for editorial 
purposes, even through their exact scores differed slightly. Individual values for all aircraft arranged by 
mission area are included in Appendix F. All of the mission groups are dominated by newly operational 
or programmed aircraft, not suprisingly. As noted previously, the values on which their scores are predi- 
cated include measures of speculation and wishful thinking. Though their position atop the lists will no 
doubt be sustained, the margins of new and future systems' superiority can be expected to contract as 
operational observations become available. 

6.1.7.1 Air Defense Mission 

Table 6. 1 contains the results from the air defense mission area computations. The margin by which the 
F-15E leads the pack is a product of the fact that it is configured with six AIM-120A (AAMRAM) air-to- 
air missiles. Neither they nor the F-15E are currently in service. Likewise, the ranking of the modified 
F-4 being considered by the Israeli Air Force is based on design information only, as is that of the 
Mirage-4000. Among the operational aircraft, current versions of the F- 15 score well across the board, 
with particluarly high marks for payload potential. The F-15s carry six of the the newest models of the 
AIM-7/SPARROW. U.S. lightweight fighters (F-16, FA18L, F-20A) also fare well, their less formidable 
payload capability offset by lower vulnerability scores. The relatively low (within this group) position of 
the F-14AC despite its undisputed excellence in the interceptor role is a product of the fact that its con- 
figuration in this data set reflected the paucity of AIM-54/PHOENIX missiles available to its only opera- 
tor in the area, Iran. Just two AIM-54's were loaded on the aircraft, and even that loading is overly gen- 
erous. The three newest Soviet fighters (SU-27, MiG-29, MiG-31) place in the top grouping. The next 
highest scoring Soviet fighter (MiG-23G) is in thirty-second position, suggesting a wide generational gap. 
Final positions in the top grouping are occupied by the latest French and British entrants into the export 
market, the Mirage-2000 and the Tornado Air Defense Variant. 



105- 





Table 6.1: , 


Aircraft With 


Highest Air Defense Potential 


AIRCRAFT 


ACP a 


AF a 


TA a 


PL a 


v a 


F15E 


5.242 


1. 582 


2.042 


7. 762 


. 703 


F15C/D 
F15CFP 


4. 058 


1. 543 


2. 007 


5. 264 


. 711 


3. 985 


1.510 


2.007 


5. 953 


. 776 


F15A/B 


3. 746 


1.464 


1. 706 


5. 264 


. 732 


SU27 


3. 148 


1.474 


1. 796 


3. 692 


. 729 


F20A 


2. 843 


1. 342 


1.485 


2. 287 


. 596 


F16C/D 
MXG29 


2. 715 


1.458 


1.452 


2. 213 


. 622 


2. 554 


1.416 


. 854 


2. 808 


. 633 


FA18L 


2. 523 


1. 505 


1. 262 


2.440 


. 672 


MIR2000C/T 


2.522 


1.421 


1. 387 


2. 058 


. 636 


F14AC 


2.459 


1.439 


1. 674 


2. 991 


. 820 


MIG31 


2. 370 


1. 386 


1. 624 


2. 867 


. 820 


TORADV 


2. 360 


1.418 


1. 566 


2. 902 


. 822 


F4M0D 


2. 187 


1. 358 


1. 279 


2. 535 


. 773 


MIR4000 


2. 104 


1. 609 


1. 146 


2.046 


. 739 



6.1.7.2 Fighter Mission 

Looking at Table 6.2, generally the same aircraft are represented. However, it is interesting to note 
the positional changes, with the smaller lightweight fighters creeping closer to the top of the list and the 
gaps between them and the F-15s shrinking. The MiG-31 and the Mirage-4000 drop out of the top group 
and are replaced by the F-16A and the austerely appointed version of the F-20. Neither the F-20 nor the 
F-16A carries radar guided air-to-air missiles. Despite the consequent lower payload scores, high manev- 
erability and low vulnerability qualify these lightweight fighters for inclusion in the top group. Comparing 
just these two tables demonstrates conclusively the benefit of employing mission sensitive relational values 
in a quantitative assessment of this type. Without them, operationally or environmentally pertinent con- 
siderations are overlooked to preserve statistical simplicity. The measuring instrument is leaner but inca- 
pable of detecting the legitimate and force posture relevant capabilities variations depicted in these two 
tables. 



106 





Table 6.2: 


Aircraft With 


Highest Fi 


ghter Potential 




AIRCRAFT 


ACP f 


AF f 


TA f 


PL f 


V f 


F15E 


3. 934 


1.576 


1. 762 


5. 612 


. 726 


F15C/D 
F15CFP 


3. 065 


1. 520 


1. 720 


3. 754 


. 739 


3.005 


1. 503 


1. 720 


4. 186 


. 795 


F15A/B 


2. 800 


1.423 


1.469 


3. 754 


. 764 


F20A 


2. 576 


1. 382 


1. 284 


2.001 


.594 


F16C/D 


2. 392 


1.476 


1. 256 


1. 877 


. 633 


SU27 


2.260 


1.460 


1.543 


2. 194 


. 757 


FA18L 


2. 185 


1.508 


1.097 


2.026 


. 692 


F16A/B 
TORADV 


2. 158 


1.513 


.834 


1. 726 


. 614 


2. 130 


1.403 


1. 364 


2.501 


. 806 


MIR2000C/T 


2. 130 


1.414 


1. 202 


1. 631 


. 657 


F20 


2. 125 


1. 393 


1.284 


1.478 


. 649 


MIG29 


2.057 


1.436 


. 756 


1. 968 


. 653 


F14AC 


2. 045 


1.427 


1.454 


2.426 


.849 


F4M0D 


1.880 


1. 350 


1. 124 


2. 156 


. 802 



6.1.7.3 Interdiction Mission 

Moving to the first air-to-ground category, Table 6.3 lists the aircraft with the best potential in the inter- 
diction role. Again, the programmed F-15E, the first of that series designed specifically as a true multi- 
role aircraft, is at the top. F- 15 variants which have only a secondary air-to-ground role move toward the 
bottom of the group, their positions taken by multi-role fighters characterized by relatively small size, high 
performance qualities, and substantial although not superior ordnance carrying capacities. The exceptions 
are the modified F-4 and the Interdiction Variant of the Tornado. The former is planned to have signifi- 
cantly greater range and ordnance capabilities than existing F-4's, and the latter was designed specifically 
for the air-to-ground mission. Note the presence of only one Soviet fighter, the SU-27, in this group, 
suggesting an apparent lack of emphasis in Soviet design on those qualities most important in conducting 
interdiction operations. 



- 107 - 





Table 6.3: 


Aircraft With 


Highest Interdiction Potential 


AIRCRAFT 


ACPi 


AF i 


TA i 


PL i 


V i 


F15E 


2. 760 


1.438 


1. 379 


2. 637 


. 669 


F16C/D 
FA18L 


2. 374 


1. 329 


1.023 


1. 837 


. 589 


2.272 


1. 374 


.882 


2. 066 


. 634 


F16A/B 
MIR2000C/T 


2.261 


1. 352 


. 716 


1. 837 


. 571 


2. 190 


1. 300 


. 981 


1. 660 


. 599 


F4M0D 


2. 150 


1. 195 


. 941 


2.498 


. 730 


F20A 


2. 068 


1. 238 


. 928 


1. 327 


. 559 


MIR4000 


2.026 


1. 360 


. 842 


2. 069 


. 703 


F15C/D 
F15CFP 


2.024 


1.414 


1.227 


1. 480 


. 676 


1. 951 


1. 388 


1.227 


1. 694 


. 737 


KFIRC7 


1.898 


1.262 


. 705 


1. 593 


. 619 


TORIDS 


1. 897 


1.291 


.874 


2. 160 


. 764 


F15A/B 


1. 848 


1. 331 


1.055 


1.480 


. 694 


SU27 


1.831 


1.205 


1. 106 


1.487 


. 693 


F20 


1. 790 


1.245 


. 928 


1. 327 


. 646 



6.1.7.4 Close Air Support Mission 

A review of the close air support mission group in Table 6.4 reveals some suprising results when viewed 
out of context. It is highly unlikely, for instance, that F-15's would be employed in a close air support 
role, although they possess attributes awarded high utility values by the aircrew survey. Their inclusion in 
the list does not imply employment in that role in force level aggreggations, it merely reflects theoretical 
potential. The absence of traditional CAS aircraft such as the A-7, A- 10, and SU-25 is also noteworthy. 
Their positions below the top grouping are strictly a product of their higher vulnerability to detection and 
engagement. The A-10A, for example, was second only to the F-15E in total payload potential, but its 
vulnerability to enagement was almost twice as high due to its relatively lower speed and maneuverability. 
With the exception of these structural anomalies, the CAS listing again shows the high mission potential 
of small, lightweight fighters with good payload capacities, maneuverability, and speed. 



- 108 





Table 6.4: 


Aircraft With 


Highest CAS Potential 




AIRCRAFT 


ACP C 


AF C 


TA C 


PL c 


V c 


F15E 


3. 115 


1.529 


. 749 


2. 764 


. 560 


F16A/B 


2. 743 


1.423 


.482 


1.842 


.462 


F16C/D 


2. 702 


1. 388 


. 596 


1.842 


.480 


F20A 


2. 651 


1. 300 


.462 


1. 691 


.440 


FA18L 


2. 593 


1.445 


. 509 


2. 046 


. 525 


F15C/D 
F4M0D 


2.410 


1.482 


. 573 


1. 998 


. 570 


2.401 


1.235 


. 587 


2.401 


. 612 


F15CFP 


2. 362 


1. 518 


. 573 


2. 146 


. 610 


F20 


2. 329 


1. 310 


.462 


1. 691 


. 502 


MIR2000C/T 


2. 251 


1. 316 


.566 


1.461 


.497 


F15A/B 
F16CSC 


2. 247 


1. 367 


.509 


1. 998 


. 588 


2. 103 


1.414 


. 340 


1. 632 


. 539 


MIR4000 


2. 068 


1.430 


.515 


1. 709 


. 594 


KFIRC7 


2.035 


1.292 


. 379 


1.432 


. 509 


F4EF 


1. 944 


1. 120 


.410 


1. 936 


. 616 



6.2 Force Propagation 

6.2.1 General Comments 

The technical combat potential of air weapons systems is only realized in their employment. The force 
propagation side of the air combat potential equation addresses those factors which govern the quantity of 
available technical potential which a national air force might generate under optimum conditions in spe- 
cific missions areas. As noted earlier, no attempt will be made to assess the relative operational, com- 
mand and control, or support proficiency of individual nations in this study. Those factors constitute fer- 
tile ground for research, and values derived from such research could modify the suboptimal results 
produced here. In this effort, operational, command and control, and support capabilities will be assumed 
to be equal. 

Accepting this assumption, four elements need to be considered in assessing an air force's propaga- 
tion potential: the numbers of specific air weapon systems on hand, the fraction that will be available for 
employment, the role(s) in which they will likely be employed, and the number of times per day which 
they can be flown. The final product of these four elements describes the daily sortie potential (SP ) for 
each system in its probable combat role(s). To keep the problem manageable, sortie potential will be 
calculated for a single day, representing the first day of combat. Surge operations are postulated over a 15 
hour flying day, with no combat or maintenance losses considered and all non-essential maintenance 
deferred. While these conditions are unrealistic, they serve the purpose of defining the outer boundary of 

-T+++tttttttttttttt + t 

2 

A detailed combat assessment model would have to include the effect of multi-day operations, losses, 

and maintenance deferrals. Operations analysts regularly employ methodologies which consider these 

and other variables in analyzing specific cases. However, the construction of a detailed combat model 

- 109 - 



a nation's force propagation potential. 

6.2.2 Available Inventory in Role 

The number and type of aircraft on hand were tabulated in the the air inventory file along with an indica- 
tor of the primary mission to which they are assigned. Also in the file was an operational availability rate 
estimated at the force level. Determining the number of aircraft available for employment is simply a 
matter of multiplying the system inventory in a given year (INV t ) by the operational availability rate 
(OAR). For instance, of the 32 F-16C's Israel will possess in 1988, 29 would be available for combat at 
an operational availability rate of 0.9. 

Allocation of aircraft to employment roles (AL ) is a bit more cumbersome. Unit employment codes 
are geared to a generic mission category (e.g., fighter ground attack) which, for the most part, subsumes 
two mission areas (interdiction and close air support in the case of ground attack fighters). One unit type, 
multi-role fighter (FMR), encompasses all four. Without a specific combat scenario, aircraft are allocated 
equally across mission areas, with two notable exceptions. Bomber aircraft are cast only in an interdiction 
role, their effectiveness in close air support being suspect. Israeli F-15's assigned to multi-role units arc 
assumed to perform primarily in the air-to-air roles for which they are best suited and not at all in the 
close air support role. To acknowledge their deep interdiction potential, 20 percent of the available 
Israeli F-15's are allocated to that role. The remainder are equally distributed between the air defense and 
fighter missions. In equation form, operationally available inventory in role (OI rt ) is calculated, 

OI rt = INV t *OAR*AL r 
The number of IAF F-15C's allocated to the fighter role on a combat day in 1988 would be computed, 

OI^ 8 = (32*.9*.4) ' 

OI f88 " 1L5 - J 

6.2.3 Sortie Rates 

The number of mission area sorties an aircraft can fly in a given day (SR ) is determined by the length of 
the flying day (LOD), the duration of the mission (MD ), the time the aircraft spends on the ground taxi- 
ing and arming (GT.), and the time required to accomplish necessary maintenance (MT). Other factors 

ttttttttttttTttttfTtt 

is beyond the purview of this research project and would outstrip its resources. 

3 

In actuality, each svstem would have differing operational availibility rates. If credible operational 

availability data could be gathered across the spectrum of systems and countries being considered, they 
would provide a more relined product. In their absence, a' gross force level estimate' will have to suf- 
fice. 



4 
5 



The F- 15 is too expensive and uniquely capable an air-to-air system to be thrust into the heavy ground 
defense environment which confronts CAS missions. 

Operations analysts at Northrop's Aircraft Division generously provided the outline of a simplified 
technique for estimating sortie rates. Their suggestions" were essential in identifying the relevant factors 
and presenting a potential computation formula. Appendix B to Epstein' Measuring Military Power 

- 110- 



associated with availability of parts and supplies are also important, but will be assumed to be be equal 
across forces in this study. The length of the flying day has been stipulated to be 15 hours. Mission 
duration varies considerably as a function of environment and mission role. The environment was 
assumed to be equal for all forces and missions. Nominal mission durations were assigned subjectively by 
category. They ranged from a low of .75 hours for a close air support mission to a high of 2 hours for a 
deep interdiction mission. It is recognized that these values would be significantly different in a confron- 
tation between Israel and Syria as opposed to one between Egypt and Libya, where greater distances 
would come into play. The mission durations used in these calculations represent regional averages and 
can be easily modified for country specific analyses. Ground time was estimated to be 45 minutes for air- 
to-air missions and 75 minutes for air-to-ground missions, which require more elaborate arming. 

Three factors needed to be considered in estimating maintenance time for an aircraft flying a particu- 
lar mission (MT ): the hours flown on the mission (MD), the man-maintenance hours required to sup- 
port one flying hour for the aircraft (MMHFH), and the maintenance personnel available for each aircraft 
(MXP). Since these had all been compiled previously, it was left to insert them in the equation, 

MT r = (MD r *MMHFH)/MXP 
To demonstrate its use, values for a MiG-21JKL operated in a fighter role by the Syrian Air Force are 
inserted in the equation. 

MT f = (1.5*18)/10.45 

MT f = 2.584 
Thus, just over two and one half hours of maintenance time would be required between each mission. 

If the effectiveness of maintenance personnel were to be considered, the MXP term would have to be 
modified by the support quality factor extracted earlier. This indexed value (Israel = 1) would be applied 
to the denominator in the formula. In the case of Syria, the support quality index value is .600. Conseq- 
uently, the maintenance ground time for the same MiG-21JKL in a fighter role would increase to 4.306 
hours if the force quality indicator were included. Unfortunately, the force quality values are low- 
confidence estimates and will be employed just to demonstrate their effect. 

The determination of a potential sortie rate for an aircraft and mission combination in the context of 
a 15 hour flying day is a matter of inserting the above identified values in the equation, 

SR f = LOD/(GT r +MT r +MD r ) 
To again use the example of the Syrian MiG-21JKL in the fighter role, 

SR f = 15/(.75 + 2.584+ 1.5) 

SR f = 3.103 

ttttttttttttttttttttt 

provided an an alternative methodology. The technique employed here borrows from both. 

- Ill - 



If the force quality modifier were considered, the potential sortie rate would decrease to to less than 2.5 
per day. 

6.2.4 Sortie Production 

The number of sorties which an air force could potentially generate in each mission area on a given day 

can be determined by multiplying the number of aircraft available for a mission area by the system's sortie 

rate in that role. In mathmatic notation, the computation is, 

SP^ = OL/SR 
rt rt r 

Substituting values for an Israeli F-15C in the fighter role in 1988, 
SP^g = (11.5*1.7) 

SPfS8 = 1955 
Again, the fractional values represent an average and could be truncated if desired. 

Table 6.5 lists total one-day sortie production by mission for 21 Middle Eastern and North African 
countries in 1988. The numbers in the far right column sum the total sorties across mission roles. The 
figures are uncontrolled for maintenance force quality, so some of the sortie production totals are consid- 
erably higher than would probably be the case in actual circumstances. 

It could be observed that the overall Israeli sortie rate across missions (2.2) is lower than advertised 
performance in the Yom Kippur War. This possible anomaly can be explained by three factors. The 
average sortie durations used in the region wide computation are longer than were flown in 1973, and the 
flying day is shorter. Additionally, a substantial portion of the Israeli force is allocated to the more time 
consuming interdiction and close air support missions. While the Syrians could potentially (quality of 
manpower being equal) produce nearly as many total sorties, the mix is quite different. Israel could gen- 
erate nearly twice as many air-to-ground sorties, with Syrian sortie production concentrated in the air-to- 
air missions. Iraq, Egypt, Saudi Arabia, Algeria, and Jordan, in descending order, are the only other 
countries in the region with a substantial sortie production capability. With the exception of Jordan, the 
estimates for the other countries in this group would be depreciated significantly if maintenance quality 
were included in the calculation. Table 6.5 also illustrates a point often made concerning the relatively! 
low threat posed by Libya's disproportionately large and difficult to maintain inventory. With a low 
operational availability rate and a small native maintenance pool, Tripoli cannot propagate a credible 
number of sorties without enormous quantities of outside assistance. Several of the Gulf States also show 
discouragingly low sortie production, largely as a factor of small maintenance pools which have not kept 
pace with the influx of aircraft. 

ttttttttttttttttttttt 

Lebanon was omitted from this and other tables, since none of its aircraft are currently operational and 
there are no indications as to when that situation might change. 

- 112- 





Table 6.5: Daily 


Sorties By 


Mission - 


1988 




COUNTRY 


INVEN- 
TORY 


ADX 


FTR 


INT 


CAS 


TOTAL 


Algeria 


266 


79 


55 


31 


106 


271 


Bahrain 


12 


2 


2 


1 


3 


8 


Egypt 
Ethiopia 


419 


174 


121 


60 


136 


491 


150 








22 


52 


74 


Iran 


47 


13 


9 


32 


60 


114 


Iraq 


556 


279 


196 


78 


177 


730 


Israel 


544 


337 


237 


204 


422 


1200 


Jordan 


130 


29 


20 


48 


139 


236 


Kuwait 


89 


11 


8 


5 


24 


48 


Libya 


530 


23 


15 


9 


34 


81 


Morocco 


93 








30 


136 


166 


Oman 


50 


6 


4 


9 


50 


69 


Qatar 


22 








2 


6 


8 


Saudi Arabia 


214 


60 


41 


65 


144 


310 


Somalia 


64 


7 


5 


7 


22 


41 


Sudan 


49 


10 


7 


11 


38 


66 


Syria 


528 


445 


317 


114 


241 


1117 


Tunisia 


22 








6 


29 


35 


UAE 


67 


10 


7 


7 


30 


54 


North Yemen 


73 


5 


4 


4 


9 


22 


South Yemen 


104 


22 


15 


6 


15 


58 



6.3 Combat Force Potential 

The ultimate step in the assessment process is to meld the two branches into a value which which cat- 
egorizes a nation's relative potential to conduct combat air operations under the employment considera- 
tions stipulated. This step transforms input data into a mission relevant potential combat output. Math- 
metically, the process is straightforward. 

CFP nrt = ACP/SP nrt 
nrt r nrt 

where, 

CFP^ = Combat Force Potential for Country n in Role r in Year t 

ACP = Air Combat Potential for an Aircraft in Role r 

SP^ = Sortie Production for Country n in Role r in Year t. 
Substituting the values for a Syrian Air Force MiG-29 employed in the fighter role in 1988, 

CFP^g - 2.057*21.58 

CFP f88 = «" 9 
Calculations are accomplished for each air weapon system in the inventory. The results can be eval- 
uated individually or aggregated for the entire national force. Table 6.6 lists the 1988 combat force poten- 
tial assessments for the Israeli and Syrian Air Forces in 1988. In this table, the quality of the respective 
maintentance forces is assumed equal. Force totals are summed at the bottom of each column. 

- 113 - 







Table 6.6: 


Comparative 


Force Potential - 1988 










AIRCRAFT 


INVEN 


TYPE 


ADX 


FTR 


INT 


CAS 




-TORY 


























ISRAEL 














A4H 


18 


FGA 


C 


1 





8. 


33 


25. 


71 


A4N 


50 


FGA 


C 


) 





26. 


26 


77. 


19 


F15A 


18 


FMR 


51. 


72 


26. 


72 


6. 


49 







F15B 


2 


OCU 


5. 


72 


2. 


96 


_ 


72 







F15C 


32 


FMR 


114. 


27 


59. 


97 


14. 


54 







F16A 


62 


FMR 


73. 


41 


55. 


93 


42. 


79 


116. 


13 


F16B 


8 


OCU 


9. 


47 


7. 


23 


5. 


55 


15. 


10 


F16C 


54 


FMR 


99. 


45 


61. 


49 


44. 


60 


111. 


29 


F16D 


8 


OCU 


14. 


66 


9. 


06 


6. 


61 


16. 


51 


F4EF 


100 


FMR 


80. 


12 


49. 


90 


39. 


71 


115. 


53 


KFIRC2 


120 


FMR 


111. 


00 


84. 


61 


77. 


93 


188. 


33 


KFIRC7 


72 


FMR 


98. 


19 


70. 


22 


58. 


41 


132. 


12 


TOTAL: 


544 




658. 


01 


428. 


09 


331. 


94 


797. 


91 








SYRIA 














MIG17F 


36 


FGA 


C 


) 





7. 


59 


23. 


18 


MIG21F 


72 


FIN 


76. 


87 


65. 


21 


C 


) 







MIG21JKL 


84 


FIN 


112. 


61 


94. 


66 


C 


1 







MIG21UM 


20 


OCA 


22. 


49 


19. 


03 


c 


) 







MIG23B 


24 


FIN 


25. 


80 


17. 


23 


c 


) 







MIG23E 


48 


FIN 


43. 


13 


33. 


24 


c 


) 







MIG23F 


70 


FGA 


C 


) 





19. 


09 


53. 


95 


MIG23G 


36 


FIN 


44. 


13 


29. 


24 


c 


) 







MIG23UM 


10 


OCG 


C 


1 





c 


1 


9. 


45 


MIG25 


38 


FIN 


36. 


44 


23. 


20 


c 


) 







MIG29 


24 


FIN 


77. 


67 


44. 


39 


c 


) 







SU22 


42 


FGA 


C 


) 





32. 


60 


75. 


23 


SU25 


24 


FGA 


C 


1 





9. 


28 


28. 


47 


TOTAL: 


528 




439. 


14 


326. 


20 


68. 


56 


190. 


28 


Note: Undepreci 


ated for Maintenance Quality 











Reflecting back to Table 6.5 which showed the two countries with nearly equal undepreciated sortie 
production, the impact of air weapon system quality is vividly demonstrated. While Syria could poten- 
tially generate 30 percent more air defense sorties than Israel in a single day of surge flying, the quality of 
its aggregate output in that mission category is one-third less. Roughly 60 percent of Syria's air defense 
force is comprised of older MiG-21 aircraft, while the least capable Israeli aircraft flying the mission is the 
F-4EF, an aircraft which has significantly greater target acquisition and payload capabilities. Even the 
projected addition of two squadrons of MiG-29's to the Syrian inventory is not enough to olfset the 
advantage accruing to Israel through superior air weapons system technology. Table 6.6 also illustrates 
Syria's relative impotence in providing air support to its ground forces. Even with the SU-25 added to its 



114 



inventory, Syrian capabilities in the interdiction and close air support roles are dwarfed by the Israeli 
potential. The Israeli MATMON B air development plan, drafted in the wake of the 1973 War, estab- 
lished creation of an air force capable of striking with overwhelming power anywhere in the region as a 
prime goal. This analysis reflects the attainment of that goal. As will later be seen, the IAF has built an 
air-to-ground capability unmatched by Syria or any other country in the region. 

If the estimated quality of maintenance support is considered, the margin of Israeli superiority in all 
mission areas becomes even more pronounced. Table 6.7 depicts 1988 combat potential depreciated for 
maintenance quality. The IAF would have almost a 2:1 superiority measured in Air Combat Potential 
Units in the combined air-to-air missions and nearly a 6:1 margin over Syria in the air-to-ground roles. 

Looking to the region as a whole, Table 6.8 depicts the aggregated 1988 combat potential scores for 

8 
21 Middle Eastern/ North African countries. Any number of observations could be drawn from this 

chart. Overall, projected air combat potential development for all countries except Israel appears to have 
focused primarily on the creation of credible air defense and air superiority capabilities. Syria, Saudi Ara- 
bia, Iraq, and Egypt all will have amassed significant air-to-air combat potential by 1988 under projected 
acquisition plans. Development of commensurate air-to-ground capabilities has lagged. Two factors 
contribute. First, the aircraft, current and projected, acquired by Soviet clients in the region simply trail 
their western produced counterparts in air-to-ground potential. Second, the primary western supplier, the 
United States, has demonstrated a political reluctance to export significant quantities of capable air-to- 
ground aircraft to states which might pose a potential threat to Israel. 

As a result, the combined air forces of Syria, Saudi Arabia, Jordan, and Iraq still fail to attain the 
levels of interdiction and close air support potential credited to Israel in 1988. It should be noted that 
mission capabilities are not operationally matched in combat, with the possible exception of air superiori- 
ty, and do not exist in a vacuum. Thus, the combined Arab lead in air defense potential should be oper- 
ationally considered in the context of Israeli interdiction potential. Similarly, the preponderance of Israeli 
close air support capability is partially offset by the numerically superior ground forces Arab states could 
theoretically commit. 

In the critical Persian Gulf, the Saudi acquisition of the Tornado package will boost its capabilities, 
in asscociation with other members of the Gulf Cooperation Council, to a position of parity with the 
other dominant air power in the region, Iraq, by 1988. In North Africa, Egyptian potential overwhelms 

-r-+-f-+++-j"f"|-+++-j-+++-!"r-f"f"f 

7 . 

Since the measure of maintenance quality is indexed to the Israeli raw value, the Israeli figures are 

unchanged from the previous table. 

8 

A full listing of nationally agcregated combat potential scores differentiated by mission for the 1984 - 

1990 time frame can be found" in Appendix G. 

9 

This example does not imply that the combined combat potential of those Arab states could be 

cumulatively brought to bear against Israel. Although such an asssertion is occassionaly made in tiring 

the political kettle, it consitutes a logistic, command and control, and intra-Arab political impossibility. 

- 115- 







Table 6.7: 


Comparative 


Force Potential - 1988 










AIRCRAFT 


INVEN 


TYPE 


ADX 


FTR 


INI 


i 


CAS 






-TORY 


























ISRAEL 














A4H 


18 


FGA 


C 


) 


C 


) 


8. 


33 


25. 


71 


A4N 


50 


FGA 


C 


) 


C 


) 


26. 


26 


77. 


19 


F15A 


18 


FMR 


51. 


72 


26. 


72 


6. 


49 







F15B 


2 


ocu 


5. 


72 


2. 


96 


. 


72 







F15C 


32 


FMR 


114. 


27 


59. 


97 


14. 


54 







F16A 


62 


FMR 


73. 


41 


55. 


93 


42. 


79 


116. 


13 


F16B 


8 


OCU 


9. 


47 


7. 


23 


5. 


55 


15. 


10 


F16C 


54 


FMR 


99. 


45 


61. 


49 


44. 


60 


111. 


29 


F16D 


8 


OCU 


14. 


66 


9. 


06 


6. 


61 


16. 


51 


F4EF 


100 


FMR 


80. 


12 


49. 


90 


39. 


71 


115. 


53 


KFIRC2 


120 


FMR 


111. 


00 


84. 


61 


77. 


93 


188. 


33 


KFIRC7 


72 


FMR 


98. 


19 


70. 


22 


58. 


41 


132. 


12 


TOTAL: 


544 




658. 


01 


428. 


09 


331. 


94 


797. 


91 








SYRIA 














MIG17F 


36 


FGA 


C 


) 


C 


) 


5. 


69 


18. 


50 


MIG21F 


72 


FIN 


57. 


76 


48. 


07 


C 









MIG21JKL 


84 


FIN 


84. 


61 


69. 


77 


C 









MIG21UM 


20 


OCA 


16. 


90 


14. 


03 


C 









MIG23B 


24 


FIN 


17. 


78 


11. 


70 


C 









MIG23E 


48 


FIN 


29. 


90 


22. 


69 


C 









MIG23F 


70 


FGA 


C 


) 


C 


) 


13. 


00 


38. 


72 


MIG23G 


36 


FIN 


30. 


42 


19. 


86 


C 









MIG23UM 


10 


OCG 


C 


) 


C 


) 


C 




6. 


87 


MIG25 


38 


FIN 


25. 


58 


16. 


03 


C 









MIG29 


24 


FIN 


56. 


07 


31. 


48 


C 









SU22 


42 


FGA 


C 


) 


C 


) 


23. 


22 


56. 


90 


SU25 


24 


FGA 


C 


I 


C 


) 


6. 


91 


22. 


56 


TOTAL: 


528 




319. 


02 


233. 


63 


48. 


82 


143. 


55 


Note: Depreciated for 


Maintenance 


Qual: 


-ty 











that which could be generated by Libya without tremendous assistance from the Soviet Bloc. To the 
south, Sudan's potential in all missions is modest and does not match the air-to-ground potential available 
to Ethiopia, while Somalia lacks a significant capability in all but the close air support roles. Across the 
Bab-el- Mandeb, North Yemen would clearly require assistance from Saudi Arabia to contest South Yem- 
en's superiority in all mission areas. Finally, there is no doubt that Algeria will maintain a dominant air 
position in the Maghreb. The Tunisian and Moroccan air forces are simply too small and too undere- 
quipped to pose a credible match. 



- 116 





Table 6.8: Combat Mission Potential 


- 1988 








COUNTRY 


INVEN- 


ADX 


FTR 


INT 


CAS 




TORY 


















Algeria 


266 


69. 


17 


50. 


88 


15. 


85 


59. 


68 


Bahrain 


12 


1. 


93 


1. 


53 


1. 


16 


3. 


72 


Egypt 
Ethiopia 


419 


202. 


51 


145. 


21 


36. 


58 


107. 


27 


150 





C 


) 


12. 


59 


38. 


62 


Iran 


47 


25. 


55 


15. 


63 


25. 


80 


66. 


35 


Iraq 


556 


247. 


39 


190. 


79 


64. 


85 


177. 


67 


Israel 


544 


658. 


01 


428. 


10 


331. 


95 


797. 


91 


Jordan 


130 


46. 


34 


32. 


53 


43. 


73 


152. 


29 


Kuwait 


89 


16. 


37 


11. 


55 


2. 


98 


17. 


76 


Libya 


530 


25. 


86 


17. 


22 


8. 


99 


30. 


49 


Morocco 


93 





C 


) 


34. 


25 


114. 


47 


Oman 


50 


14. 


26 


8. 


90 


8. 


48 


37. 


89 


Qatar 


22 





C 


) 


1. 


99 


6. 


14 


Saudi Arabia 214 


226. 


56 


120. 


31 


71. 


53 


199. 


05 


Somalia 


64 


3. 


45 


2. 


79 


2. 


20 


8. 


35 


Sudan 


49 


7. 


11 


5. 


91 


5. 


29 


20. 


81 


Syria 


528 


439. 


14 


326. 


21 


68. 


55 


190. 


29 


Tunisia 


22 





C 


) 


6. 


07 


23. 


56 


UAE 


67 


26. 


30 


15. 


14 


3. 


21 


16. 


03 


North Yemen 


73 


3. 


39 


2. 


71 


2. 


66 


8. 


05 


South Yemen 


104 


19. 


38 


13. 


01 


5. 


67 


16. 


27 


Note: Undepreciated 


for Maint 


enance Qua. 


Lity 









6.4 Summary 

These thumbnail analyses are representative only and by no means exhaust either the relevant questions 
pertaining to air development in the region or the analytical potential of the assessment methodology. 
Further examples will be offered in Chapter 7 which exercise these application attributes. What this 
chapter has demonstrated is that an analytical regimen which countenances the combined contributions of 
technical capability and force propagation to potential output in specified air combat roles is a viable 
assessment tool. The elimination of any one of these considerations (technical potential, mission rele- 
vance, propagation potential) leads to conclusions which lack military and, to some extent, political rele- 
vance. One may quarrel legitimately with individual input values in this data set and with the assump- 
tions under which they were combined; but there can be no argument as to the essentiallity of their 
consideration in an analysis which attempts to measure the effect of weapons transfers on national air 
combat capabilities or regional balances. 



- 117 



Chapter 7 
POLICY ASSISTANCE APPLICATIONS 

The goal of this research was to develop a military analysis tool which could assist policy makers in 
developing, evaluating, and supporting security assistance packages. The mechanism has been described 
and implemented and some individual results highlighted, but its efficacy in producing decision relevant 
data still needs to be established. The model as it stands produces results dictated by the input data and 
underlying assumptions. As such, its output is static and conceivably unresponsive to the problems, pri- 
orities, and perceptions of a user evaluating a specific security assistance question. In Chapter 1, it was 
noted that a model which could not be molded to meet user denned criteria would inevitably fail to gen- 
erate policy relevant results. To avoid this pitfall, features have been included in this methodology which 
permit user directed modifications of assumptions and, in many instances, of input data. This chapter will 
demonstrate the sensitivity of these features in evaluating a security assistance question and suggest some 
additional categories of questions to which it could be directed. 

7.1 Criteria 

E. S. Quade, in his discussion of the role of analysis in supporting policy decisions, posits a cycle which 
an analytical regimen must transit. He describes a ten step process which begins with the determination 
of analytical objectives and criteria, flows through data collection and model design, applies the model to 
assessing alternatives for evaluation and interpretation, and ends with the reassessment o( assumptions and 
alternatives for reintroduction into a subsequent analytical phase. Without delving into the paradigm s 
elements too deeply, two key concepts bear mention in the context of this effort. Most significantly, the 
analytical process is iterative. It must accommodate the introduction of evolving alternatives and chang- 
ing assumptions if it is to present the decision maker with options pertinent to his problem. The model 
which it employs must, therefor, be adjustable at each phase of its operation. The interpretation of ana- 
lytical output demands decision maker participation, the effectiveness of which is largely a product of liis 
appreciation of the methodology's assumptions, input data, and combinational scheme. To question and 
change any of these essential elements, the decision maker must have access to them and be able to make 
alterations to suit his requirements. The methodology proposed for assessing the impact oi air weapon 

systems' transfers on recipient force structure and regional military balances possesses those attributes 

ttttttttttttttttttttt 

i 

See Quade, Analysis for Public Decisions, pp. 50-66 for a thorough discussion of the steps in policy 

analysis and their interrelationships. 

- 118 - 



which permit the decision maker not only to test alternatives but also to alter the conditions under which 
they are tested. 

The analytical example offerred in the next section is geared to illustrate the methodology's flexibility 
in responding to hypothetical decision maker directed changes at various junctures in the analytical pro- 
cess. In particular, the capability to modify input data and underlying assumptions is emphasized, along 
with the potential to derive new alternatives and evaluate their effectiveness. Methodological results will 
be interpreted strictly on their own merits, recognizing full well that the actual interpretation process 
would by necessity involve a host of considerations exogenous to the model. 

7.2 Enhancing Jordanian Air Combat Potential 

Rather than trekking through a series of discrete problems, this example will consider a single secunty 
assistance question and its permutations. The security assistance dilemma presented by Jordan's require- 
ment for an advanced air defense fighter embodies many of the elements which confound arms transfer 
policy makers. Jordan is a long- time American arms client whose strength and stability are critical to 
regional security. It is threatened sporadically by a much more powerful neighbor, Syria, whose Soviet 
patronage and radical tendencies are antithetical to Washington's regional objectives. Jordan is also puta- 
tively threatened by Israel, whose policy of aggressive deterrence includes regular overflights of Jordanian 
territory. Conversely, Jordan itself is viewed as a threat by Israel, America's closest ally in the region. 

Consequently, any security assistance to Jordan must be evaluated not only in the context of its own 

2 
defense but also in terms of the potential threat it poses to Israeli security. 

From a military perspective, Jordan is highly vulnerable to incapacitating air attacks from either of its 
more powerful neighbors. Much of its industry is concentrated in along the Dead Sea; 60 percent of its 
agriculture is confined to the eastern Jordan Valley; and its economy is highly dependent on free access to 
the port of Aqaba. Its power and water supplies are likewise inviting air targets. Both the Syrian and 
Israeli air forces currently have the capability to overwhelm Jordan's air defense system, and those capa- 
bilities will increase over the next five years as new systems are introduced. The air component of Jor- 
dan's air defense system is currently limited to 38 Mirage F-1/B/C/E's, with which Amman is not entirely 
satisfied. 

Against this admittedly sketchy backdrop, the elements of a question to which the air capabilities 
methodology could be applied can be drawn. In 1985, Amman requested United States' assistance in 
enhancing its air defense capabilities to counter the projected threat into the 1990's. One component of 

++++T+ttT++tttttttttt 

2 

See Cordesman, Jordanian Arms and the Middle East Balance, pp. 39-42, for a discussion of threats to 
Jordan and incidents of Israeli overflights. This example will not treat the political dynamics of the 
problem or become embroiled in the debate of who threatens whom. The intent of tliis section is to 
demonstrate methodological flexibility, not to evaluate Middle Eastern political questions. The influ- 
ence of political perceptions and objectives would be applied outside of the methodology. 

- 119- 



3 
the package was a request for 40 air defense fighters. The American response is currently adrift in a 

political maelstrom, and it is not the intent of this illustration to reenergize it or advocate particular alter- 
natives. Nonetheless, the Jordanian air defense enhancement request provides a demanding vehicle with 
which to flex the proposed analytical methodology. What pertinent questions are tractable to quantitative 
military analysis? First, it can evaluate the relative combat potential of alternative air weapon systems in 
the projected employment environment. Second, it can test the impact each alternative makes on national 
air capabilities. Third, it can assess the effect of the proposed arms transfer on the regional military bal- 
ance under varying scenarios. In the Jordanian case, the first problem is to identify and evaluate the air- 
craft and configurations feasible for transfer under the constrictions imposed by the terms of the request 
and American transfer policy. 

7.2.1 Aircraft Alternatives 

Two aircraft are likely candidates to meet Jordanian requirements: the F-16C and the F-20A. In defer- 
ence to probable political restrictions, it is hypothesized that the aircraft would have to be configured in 
such a way as to preclude their effective employment in an air-to-ground role. Further, the transfer of a 
capability to launch radar guided air-to-air missiles is stipulated as being destabilizing vis-a-vis Israel. It 
might be remembered from a previous chapter that modified versions of the F-16C and the F-20A have 
already been configured in the study data set, identified as the F-16CSC and F-20 respectively. The 
F-16CSC is equipped with the AN/APG66 radar which does not have the capability to illuminate targets 
for radar air-to-air missile guidance. Additionally, the CCRP/CCIP feature of the fire control system has 
been omitted to complicate effective air-to-ground ordnance delivery. The AN/APG67 radar associated 
with the F-20 has been similarly limited, with options to support BVR radar guided missiles and enhance 
ground tracking capabilities eliminated. Both systems will be configured for the air-to-air role with four of 
the latest export version of the Sidewinder (AIM-9P), which lacks a foreward hemisphere engagement 
capability. To extend the frame of reference, a French aircraft, the Mirage- 2000C, is also evaluated on the 
surmise that it might be an alternative from the Jordanian perspective if Washington denied Amman's 
request. Of course, the French alternative would not be subject to U.S. imposed constraints; so its con- 
figuration was not altered from that already exported to other Middle Eastern states. Air-to-air combat 
potential scores were computed for each aircraft using the techniques, assumptions, and data discussed in 
earlier chapters. The results of the initial inquiry are displayed in Table 7.1. 

ttttttttttttttttttttt 

3 • • 

See Gordon, 'Administration Urges Congress to Accept Arms Sale to Jordan', for a description of the 

requested arms package and its supporting rationale. 

It needs to be clearlv understood that these particular assumptions and other like them cited in this 
example are included for the purposes of illustration only ancf do not correspond to U.S. government 
policies, perceptions, or practices. 

- 120 - 



Table 7.1: Combat Potential in Air-to-Air Roles 


AIRCRAFT AIR DEFENSE POTENTIAL 


FIGHTER POTENTIAL 


F-16CSC 1.541 
F-20 1.933 
Mirage-2000C 2.522 


1. 734 

2. 125 
2. 130 


Note: Scores computed with system defaults 





As a reminder, the numbers shown represent units of air combat potential (ACPU's) credited to the 
air weapon system alone. ACPU's are relative measurements within the confines of the study data set. 
They do not connote absolute values of independent merit. The higher scores awarded the F-20 in rela- 
tion to the F-16CSC are primarily the products of a more effective radar and a lower vulnerability to 
engagement. The fact that the F-20 has a greater gun ordnance capacity also plays a marginal role in 
producing higher ACPU ratings. These factors offset the relative superiority of the F-16CSC airframe in 
both roles. The Mirage-2000C garnered the highest ratings largely because of its equippage with radar 
guided air-to-air missiles, which are afforded a high relative utility in the air defense mission. In reviewing 
the initial findings, note that the assumptions under which the default relative utility values had been 
established were predicated on a nominal regional employment environment which did not correspond 
entirely to the situation facing Jordan. Given the compact defensive environment, it is probable that the 
range attribute is overemphasized, as is the relative utility of radar guided air-to-air missiles. To correct 
this deficiency, utility values were adjusted to lessen the impact of range and radar missile capabilities on 
the overall computation. The results of the second iteration are displayed in Table 7.2. 





Table 7.2: Combat Potential in Air-to-Air Roles 


- Revised 






AIRCRAFT 


AIR DEFENSE POTENTIAL 




FIGHTER POTENTIAL 


F-16CSC 1.703 
F-20 2.133 
Mirage-2000C 2.432 






1. 
2. 
2. 


737 
134 
147 


Note: Scores 


computed with revised utility 


values 







While the Mirage-2000C still receives superior scores due to its multiple missile type carriage, its 
margin of superiority lessens as a function of the lower relative utility awarded the radar guided missiles. 



121 - 



The impact of the changed utility values on the comparison between the F-16CSC and F-20 is negligible, 
although both score higher as a result of the modifications. If the inquiry were terminated here, it would 
appear that the F-20 represents a more favorable American alternative when only air-to-air applications 
are considered. It is also evident that either American alternative is inferior to the Mirage- 2000C when 
combat potential is considered under asymetrical political constraints in an employment vacuum. Of 
course, only the first step in the inquiry has been completed. 

7.2.2 Force Structure Impacts 

The next challenge is to measure the effect of the proposed transfers on the Jordanian air defense force 
structure. To accomplish this task, additional information needs to be extracted from the data set and 
modified in accordance with inquiry objectives. First, alternative air inventories must be formulated. 
According to a least one report, the first F-20s could be delivered within 2.5 years of a decision, with the 
full package in place within 5.5 years. Initial F-16CSC deliveries would be delayed an additional year. 
Information concerning Mirage-2000C production schedules was not available, so it was assumed first 
deliveries could take place within three years of an order. For the sake of the illustration, it was postulat- 
ed that all deliveries would be completed by 1990, a risky assumption in the case of the F-16CSC, but one 
which is suitable to the demonstration. In deference to data base limitations, it will be assumed that the 
notional analysis is being conducted in response to the initial request, with a decision anticipated before 
the end of 1985. 

Based on the above, F-20's were introduced into the Jordanian inventory begining in 1988, with all 
40 delivered by 1990. All 40 F-16CSC's were also forecast to be in place by the end of that year, as were 
all the Mirage-2000C's, the delivery of which would have begun in 1989. The results of the force level 
computations are displayed in Table 7.3 Again, a couple of reminders might be useful. The capabilities 
embodied in the transfers under study are integrated into a pre-existing force structure, so the Air Combat 
Potential Unit ratings constitute aggregated totals. Additionally, the force level computations include a 
sortie generation algorithm which considers an aircraft's maintenance requirement (man maintenance 
hours/flying hour) and mission specific sortie lengths. Consideration of these factors creates even greater 
differentiation among the options than was exhibited when the sterile air weapon system ratings were 
examined. 

Regarding this table, additional dimensions of the assessment process come into focus. First, the 
earlier availability of the F-20, if accurate, provides a more immediate payoff. Second, the low mainte- 
nance overhead associated with the F-20 permits a higher sortie generation rate which more than com- 
pensates for the higher weapon system scores received by the Mirage- 2000C. On the basis of this force 
level analysis, it appears that the F-20 represents the most effective air-to-air combat choice for the Royal 
Jordanian Air Force, even when the French option is considered. 

- 122- 



Table 7.3: Jordanian Air-to-Air Combat Potential - Options 




1988 


1989 


1990 


F-16CSC Package 
Air Defense 
Fighter 
Total Air-to-Air 


45. 67 
32. 35 
78.02 


45. 67 
32. 35 
78.02 


100. 34 

82. 71 

193. 05 


F-20 Package 
Air Defense 
Fighter 
Total Air-to-Air 


78. 14 

57.24 

135.38 


109. 93 

81. 96 

191.89 


152. 32 
114. 92 
267. 24 


Mirage-2000C Package 
Air Defense 
Fighter 
Total Air-to-Air 


45. 67 
32. 35 
78.02 


100. 92 

64.46 

165. 38 


137. 30 

85. 76 

223. 06 


Note: Computation used unmc 


idified data 


and system defaults 



7.2.3 Modifying Assumptions and Packages 

7.2.3.1 Alternate Assumptions 

Upon reviewing these results, the user might again decide that some of the input data need further revi- 
sion. For instance, it could be observed that the maintenance requirement for the F-20 (15 MMH/FH) is 
not derived from an evaluation of fielded systems and might be overly optimistic and that the F-16CSC 
estimate (23 MMH/FH) is a bit pessimistic. Consequently, the maintenance figure for the F-20 could be 
raised to match user perceptions and the F-16CSC estimate lowered. Table 7.4 displays the results of a 
computation when the maintenance requirement for the F-20 is raised by four hours and that for the 
F-16CSC is lowered by two. The recomputation places the F-16CSC in a more competitve position in 
the 1990 time frame with the Mirage-2000C, although the F-20 still enjoys a definite advantage. 



ttttttttttttttttttttt 

This statement in no way is meant to impugn the estimates made by any aircraft producer. These 
variations are included solely to demonstrate methodological flexibility.' 

- 123 - 



Table 7.4: Jordanian Air-to-Air Combat Potential - Revised 

1988 1989 1990 

F-16CSC Package 
Air Defense 
Fighter 
Total Air-to-Air 

F-20 Package 
Air Defense 
Fighter 
Total Air-to-Air 

Mirage-2000C Package 
Air Defense 
Fighter 
Total Air-to-Air 

Note: Computation used modified airframe and force level data. 



45. 
32. 
78. 


67 
35 
02 


45. 
32. 
78. 


67 
35 
02 


120. 

86. 

206. 


98 
01 
99 


75. 

53. 

129. 


91 
56 
47 


106. 

74. 

180. 


15 
78 
93 


146. 
103. 
249. 


47 
07 
54 


45. 
32. 
78. 


67 
35 
02 


98. 

64. 

162. 


30 
54 
84 


133. 

85. 

219. 


39 
99 
38 



7.2.3.2 Alternate Package Composition 

On the basis of these preliminary findings, it could be hypothesized that the F-20 package merits addi- 
tional evaluation. Table 7.5 portrays the impact of the 40 aircraft F-20 package on overall Jordanian force 
potential, this time including the air-to-ground assets. Jordanian interdiction and close air support capa- 
bilities are provided primarily by 56 F-5E's. CASA C-101's (14) join the inventory beginning in 1988 to 
accomplish the counterinsurgency mission, which is subsumed into close air support in these calculations. 
The calculations used in compiling this and subsequent tables incorporate the assumption and data revi- 
sions postulated earlier. 



Table 7.5: Jordanian Air Combat Potential 




1988 


1989 


1990 


Air Defense 75. 91 
Fighter 53.56 
Interdiction 43. 73 
Close Air Support 152. 29 


106. 15 
74. 78 
44.45 

158. 12 


146. 47 

103. 07 

44.45 

158. 12 


Total 325.50 


383.50 


452. 11 



For the sake of this demonstration, an assumption could be made that proposal of a 40 aircraft 
package would be politically inopportune but that a smaller complement might be palatable. Recognizing 



124 



Jordan's precarious security situation, it might be advisable to couple the reduced package with assurances 
of American support in case of Syrian aggression. While this hypothesis is a bit far-fetched politically, it 
would reduce Israeli sensitivities to the proposal while bolstering Jordanian confidence. A tentative secur- 
ity package was envisioned which would limit the number of aircraft to 24 but which would pledge 
American air refueling support for air defense missions and supplementary maintenance support for all 
F-20's in the case of war with Syria. Under this proposal, 12 F-20's would be delivered in 1988, with an 
additional 12 the following year, mirroring the original delivery proposal. No further deliveries would be 
accomplished. The results of this notional formulation on Jordanian air combat potential are depicted in 
Table 7.6. 



Table 7.6: Jordanian Air Combat Potential - U.S. Support 

1988 1989 1990 

Air Defense 
Fighter 
Interdiction 
Close Air Support 

Total 339.36 405.91 405.91 



87. 


02 


123. 


05 


123. 


05 


56. 


32 


80. 


29 


80. 


29 


43. 


73 


44. 


45 


44. 


45 


152. 


29 


158. 


12 


158. 


12 



The impact of aerial refueling and supplementary maintenance (20%) support can be seen most 
clearly in the air defense scores for 1988 and 1989. Potential air defense combat output in each of these 
years is significantly enhanced by the combined effects of increased endurance and greater maintenance 
resources. Fighter mission capabilities are less noticeably affected, since tankers would not be committed 
to support air superiority missions. However, the figures in the 1990 column indicate that these support 
enhancements will not fully compensate for an inventory reduced by 40 percent, even though they do 
make a dent in the potential deficit. 

In realistic terms, this particular security assistance arrangement might be a pipe-dream, but the 
potential to evaluate such complex hardware and support combinations is inherent in the analytical 
methodology. One more flexibility exercise will be conducted before moving to the regional stability 
issue. Acknowledging that Jordan is confronted with a relative deficit not only in air defense assets but 
also in ground attack resources, a final question is to evaluate the impact of the contemplated F-20 trans- 
fer insofar as it would permit the Jordanian Air Force to shift other assets to ground attack missions. 
Specifically, the F-20's might conceivably replace the current contingent of Mirage F-l's in the air-to-air 
ttttttttttttttttttttt 

According to the manufacturer, the F-20 can be equipped with an optional refueling probe. 

- 125- 



missions, with the latter re-roled as ground attack assets. Table 7.7 depicts the results of that investiga- 
tion. 



Table 7.7: Jordanian Air Combat Potential - F-l's Re-roled 

1988 1989 1990 

Air Defense 75,91 60.48 100.80 

Fighter 53.56 42.43 70.72 

Interdiction 43.73 64.37 64.37 

Close Air Support 152.29 207.12 207.12 

Total 325.50 374.40 443.01 



In this instance, the 37 F-lC/E's were reassigned to air-to-ground missions in 1989 after the first 24 
F-20's had become available for air-to-air operations. Note the substantial drop in air defense and fighter 
capabilities in 1989 which is only partially rectified with the arrival of 16 additional F-20's in 1990. At the 
same time, Jordan's interdiction potential would increase by approximately 50 percent, with close air sup- 
port capabilities climbing a more modest 25 percent. Given the Jordan's vulnerability to air attack and 
the relative superiority of its neighbors, such a conversion would be unlikely, but its effects can be fore- 
cast. 

7.2.4 Assessing Regional Stability 

Of course, force potential computations are only of passing interest when viewed outside their employ- 
ment context. The next series of assessments places a proposed 40 aircraft F-20 sale to Jordan in two 
threat environments. The first assesses the relative combat balance between Jordan and its allies against 
its most threatening neighbor, Syria. 

7.2.4.1 Jordan and Allies Versus Syria 

At the outset, it is important to recollect that the ratings represent the balances of relative potential for a 
single day of combat. They are unmodified by considerations of operational proficiency or C I support 
and should in no way be construed as predictors of combat outcome. They are static rather than dynamic 
indicators of potential combat effectiveness. To further explore system capabilities, it will be assumed that 
Saudi Arabia and Iraq will provide Jordan limited air support in a confrontation with Syria. .Amman s 
notional allies will retain all air-to-air assets for their own protection and will contribute a portion (Iraq, 
50%; Saudi Arabia, 30%) of their interdiction resources for attacks against Syria. No allied close air sup- 
port assets will be considered, since the command and control difficulties involved are be prohibitive. The 
balance of air combat potential under this scenario is shown in Table 7.8. 

- 126- 



Table 7.8: Jordanian/Syrian Air Combat Balance - Allied Support 




1988 


1989 


1990 


Jordan and Allies 








Air Defense 


75. 91 


106. 15 


146.47 


Fighter 


53. 56 


74. 78 


103. 07 


Interdiction 


60. 66 


60. 66 


60. 66 


Close Air Support 


152.29 


158. 12 


158. 12 


Syria 
Air Defense 








439. 14 


434.23 


544. 74 


Fighter 


326.21 


310. 59 


347. 32 


Interdiction 


68. 55 


69.40 


65. 60 


Close Air Support 


190.29 


192. 93 


181. 34 



Syria's preponderant superiority in air-to-air combat potential is clearly demonstrated. Its air-to- 
ground potential is considerably more modest, virtually on a par with that of Jordan and its allies. How- 
ever, the comparisons which really count in this evaluation are those between the mission roles. Syrian 
air defense forces have such a significant combat potential that the relatively weak interdiction effort which 
Jordan and its allies could launch would not likely be any more than marginally effective from a military 
standpoint. Similarly, the probability of Jordan maintaining air superiority over the battlefield would be 
remote, given the overwhelming Syrian superiority in the fighter mission category. The inability to credi- 
bly contest Syrian air superiority would severely curtail the potential effectiveness of Jordan's close air 
support assets, even though they are on a relative par with Syria's. On the plus side, the combination of 
Jordan's bolstered air defense potential and Syria's low interdiction potential distinctly diminishes the air 
threat against key targets within Jordan. All other factors being held constant, the addition of advanced 
aircraft to Jordan's air defense arsenal might well deter a Syrian air attack but would still not be sufficient 
to carry the air war to Syria or to offset Syrian ground force superiority. 

7.2.4.2 Jordan and Allies Versus Israel 

A second threat environment which must be adressed, albeit reluctantly, involves war between the Arab 
Confrontation States and Israel. The first problem is to define which states fit in the Confrontation cat- 
egory, and the composition is by no means clear. Since the study is concerned with military potential and 
not rhetoric, the Arab posture will be construed less effusively than is sometimes the practice. Syria is the 
Arab hub; and Jordan will be included only insofar as the assessment concerns the impact of arms sales to 
it. Additionally, Iraq and Saudi Arabia will be assumed to contribute the same level of support as was 
postulated in the previous scenario against Syria. With Egypt militarily and politically neutralized by the 
Camp David Accord, this line-up seems to constitute the least unreasonable of the potential threats to 



127 



Israel. 



Table 7.9: Arab/ Israeli Air Combat Balance 


• 




1988 


1989 


1990 


Jordan and Allies 








Air Defense 


526. 16 


494. 71 


645. 56 


Fighter 


382. 53 


353.02 


418. 04 


Interdiction 


165. 86 


187. 28 


183. 48 


Close Air Support 


342.58 


400. 13 


388.46 


Israel 








Air Defense 


658.01 


669. 14 


646. 84 


Fighter 


428. 10 


434. 92 


419. 70 


Interdiction 


331. 95 


328. 01 


363. 10 


Close Air Support 


797. 91 


780. 51 


746. 92 



Looking at Table 7.9, combined Syrian and Jordanian air-to-air combat potential will approach that 

8 .... . 

possessed by Israel at the end of the decade. Relative parity in the air-to-air roles would be predicated 

on Syria's acquisition of four squadrons of MiG-29's and two squadrons of SU-27's by 1990 and Jordan's 

receipt of the F-20 arms package. Israel will continue to hold a clear edge in air-to-ground mission 

potential, compensating for numerical inferiority on the ground. Evaluating the situation across mission 

areas, the picture is less clear. The Arab potential to conduct successful interdiction operations against 

Q 

Israel proper in the face of the IAF's substantial air defense capability is negligible. In the same regard, 
evolving Arab air defense potential might attenuate the hitherto unchallenged Israeli potential to conduct 
deep interdiction operations at will. Over the battlefield, air superiority potential would suggest a virtual 
standoff if other factors such as pilot skill, maintenance proficiency, and CI are held constant. Even 
when this matchup is deemed a wash, Israeli capabilities to provide air support to ground forces measura- 
bly* outstrip Arab potential to do the same. In a final comment, the organization and training of the 
Israeli Air Force give it considerably greater flexibility in asset allocation. With F-16's, F-4's, and, to a 
lesser degree, F-15's assigned to units with multi-role responsibilities, assets can be employed in combina- 
tions tailored to a particular threat scenario rather than according to the static allocations used in this par- 
ttttttttttttttttttttt 



From a political vantage point, the inclusion of Iraq and Saudi Arabia in a colleaal effort with Syria is 
improbable. From a militarv perspective, Jordan's participation would be suicidal with Egypt on the 
side-lines. This example is illustrative only, not predictive or even plausible. 



In this an other force level examples, the reader will note that total combat potential actuallv decreases 
in some years. The seemingly counterintuitive observation is a function of the replacement logic which 
decrements obsolete aircraft in unit sized increments after new acquisitions become available. When 
tabluated annually, this procedure creates some inventory overlaps which would disappear if invento- 
ries were tabulated on a monthly or quarterly basis 

Recognizing the Arab deficit in interdiction assets, Jordanian Mirage F-l's are committed to air-to- 
ground roles in this assessment of the threat to Israel. 



- 128- 



ticular computation. For instance, multi-role fighter could be withdrawn from the air defense mission 
to gain air superiorty or to launch massive interdiction campaigns if the combat situation warranted. 

To insert the impact of another dimension, quality of maintenance support, Table 7.10 depicts the 
same force balance when sortie generation potential is depreciated for relative support personnel profi- 
ciency. While the specific support index values might be challenged, there is no serious argument that 
Arab maintenance capabilities are on a par with Israel's. As can be seen from Table 7.10, the relative 
balance between the IAF and the combined Syrian and Jordanian Air Forces disintegrates when support 
personnel quality is considered. A further diminution of Arab potential would surely result from any 
appraisal which considered operator and CI proficiency as well, either quantitatively or subjectively. 



Table 7. JO: Arab/Israeli Air Combat Balance - Depreciatec 


I 




1988 


1989 


1990 


Jordan and Allies 








Air Defense 


344. 77 


365. 08 


473. 70 


Fighter 


251.56 


256. 92 


303. 39 


Interdiction 


140.47 


127. 84 


138. 53 


Close Air Support 


317. 62 


324. 75 


315. 70 


Israel 








Air Defense 


658. 01 


669. 14 


646. 84 


Fighter 


428. 10 


434. 92 


419. 70 


Interdiction 


331. 95 


328. 01 


363. 10 


Close Air Support 


797. 91 


780. 51 


746. 92 



7.2.5 Conclusions 

This string of analyses demonstrates the responsiveness of the proposed methodology in analyzing the 
military aspects of a security assistance case under a variety of assumptions. The model proved useful in 
assessing the relative merits of system alternatives, defining their impact on force structure, and evaluating 
their effect on stability in a regional context. Most importantly, the potential for user interaction at each 
phase of the process was exercised, altering computational inputs to accommodate differing perceptions or 
priorities. In this light, analytical output constitutes a flexible and comprehensive input to the interpreta- 
tion and deliberation process. 

Using the findings from this hypothetical example, for instance, one might observe that the transfer 
of a package of 40 F-20's configured for air-to-air operations is the most effective practicable response to 
Jordan's requirement for a modern air defense fighter. The F-20's would create the potential by 1990 to 
defend against Svrian air attacks on the vulnerable Jordanian heartland while not providing sufficient 



10 



Those allocations can be changed within the model to reflect differing threat perceptions, although 
this was not done in the current example. 

- 129- 



capabilities to support offensive Jordanian air operations against either Syria or Israel. The sole threat 
such a transfer appears to pose to Israel is to diminish the potential effectiveness of Israeli interdiction 
operations. When depreciating factors such as the quality of maintenance support are considered, even 
this impact on Israeli security is negligible. 

It goes without saying that these quantitatively based observations are insufficient evidence on which 
to predicate a transfer decision. Rather, they must be melded with assessments of other military factors 
such as ground based air defense capabilities, ground force combat potential, and a basket of international 
and domestic political considerations before a comprehensive policy can be elicited. Nevertheless, the 
type of quantitative military analysis capability demonstrated here is an essential element in the process. 
This fact demands that it be firmly grounded technically and methodologically, be visible to and accessible 
by the user, be adaptable to alternate configuration and computational assumptions, and capture the 
impact of security assistance programs on recipient combat potential output and regional balances. As 
illustrated, this methodology meets the demand. 

7.3 Other Applications. 

Throughout most of this investigation, the spotlight has been on the development and application of an 
assessment tool to assist arms transfer policy makers. It would be remiss, however, not to mention some 
additional applications to which it could be adapted. 

7.3.1 Air Intelligence Analysis 

The same features which make the methodology viable from a policy assistance standpoint are germane to 
some aspects of air intelligence analysis. There is no doubt that its focus on combat potential permits a 
more relevant portrayal of air capabilities evolution than does an analysis tethered exclusively to invento- 
ries. The ability to consolidate the combined influences of aircraft attributes and subsystems is even more 
valuable. The cumulative effects of the strengths and weaknesses of an air weapon system's parts are 
assessed all too infrequently in intelligence analyses which are boresighted on a handful of system charac- 
teristics. In the same vein, the impact on combat potential of upgrades to aircraft subsystems can be 
evaluated discretely or at the force level, as can alterations to force specific attributes such as mission allo- 
cation or maintenance support. The iterative capability is likewise pertinent to the process of estimating 
future threats under a variety of scenarios and force structures. As in the case of arms transfer policy 
assistance, the methodology is not sufficient in and of itself to capture the full range oi factors which 
determine threat. However, it provides exponentially more comprehensive input data to the threat 
assessment process than does a mere listing of orders of battle and isolated performance characteristics. 



- 130- 



7.3.2 Operations Research/ Analysis 

Standing alone, the methodology lacks the element of dynamic interaction inherent in most operations 
analysis models. While the latter are capable of stepping through multiple series of force on force combat 
simulations, many rely on categorical or nominal input data. Since force quality is an integral element in 
most operations analyses, system and force specific combat potential values generated by a methodology 
such as the one proposed in this study could supplant nominal measures at the front end. While no fea- 
sibility tests of this application have been conducted, it appears to be a productive avenue for additional 
inquiry. 

7.3.3 Microcomputer Processing 

Throughout the discussion, several substantive and procedural defects in the air combat potential meth- 
odology have been flagged as requiring further development. One additional deficiency is the fact that the 
model as currently constituted is cumbersome to operate. It was constructed on an IBM 3033 mainframe 
computer, using the Statistical Package for the Social Sciences (SPSS) processing system. While this 
combination provides a powerful and flexible processing environment, input data and combinational 
algorithms are not readily accessible to or modifiable by the casual user. For instance, each of the analyt- 
ical iterations described in the previous section required reprogramming of the logic and utility values in 
several different computational modules. The procedure is effective but demands intimate familiarity with 
the data sets, access procedures, and programs. To that extent, system transparency is beclouded. Initial 
tests on data sub-sets suggest that the system could be installed profitably on a microcomputer outfitted 
with data base management and spreadsheet software. 

Conceptually, a hierarchy of menu-like screens could channel processing in the direction(s) desired by 
the user and make the information which he required for a specific inquiry immediately available. Using 
dBase-II as a test vehicle, a series of menu screens were constructed, the options listed in which linked the 
user to specific data files. FUes were arranged to correspond to the progression of analytical nodes 
described in Chapter 3 (e.g., airframe, target acquisition system, inventory). Employing the file edit capa- 
bility, input data could be altered and sub-sets reserved for eventual introduction into the computational 
(spreadsheet) phase. Computational variables (e.g., relative utility variables, modifying variables) were 
established as 'look-up' tables in the spreadsheet (LOTUS 1-2-3) and could be inspected and altered by 
the user prior to score calculation. 

In execution, these procedures proved conceptually sound but tedious and at times frustrating. User 
visibility and interaction were enhanced, and the requirement to delve into specific programs was elimi- 
nated. However, processing was limited to segmented data sets and required the linking' of several 
spreadsheets. Values for computational variables could be changed with relative ease, but evaluating dif- 



- 131 



fering configurations or force alternatives required reinitiation of the entire problem definition process. In 
effect, the breadboard micro-based model proved only marginally more 'user-friendly' than the original 
system and was more time consuming. One additional deficiency stemmed from the fact that factor scor- 
ing could not be accomplished using the system configuration available. To add a new system or subsys- 
tem to a microcomputer file required regeneration of the expanded file on the mainframe system with 
results downloaded to the micro. 

Several of the problems experienced in attempting to adapt the analytical methodology proceeded 
from the technical limitations inherent in the micro itself (Z-100 with 192K, no hard disk). Others 
undoubtedly reflect the researcher's relative unfamiliarity with applicable micro software. Given these 
factors, it would be imprudent to abandon the effort to adapt a version of this methodology for micro- 
computer operation. With a more powerful processor and more flexible data base management software, 
the creation of a truly user-interactive analytcial system is eminently achievable. 



132 



Chapter 8 
SUMMING UP 

The objective of this research effort has been to develop a methodology which permits the assessment of 
the aggregated impact of air weapon systems transfers on recipient air combat potential and regional mili- 
tary balances. At the outset, it was established that a viable methodology would have to meet six criteria: 

• The methodology must be oriented toward combat relevant output not system input. 

• The contribution of weapon subsystems to combat potential must be addressed. 

• Comparison between aircraft in definable mission roles and among aggregated national forces is 
essential. 

• Input data must be valid, accessible, and free from bias. 

• Analytical procedures must be transparent and purged of sources of systemic error. 

• Analytical assumptions must be clearly delineated and amenable to user designated variation. 



8.1 Analytical Structure 

To insure compliance with the first three criteria, a matrix was developed the key elements of which con- 
stitute the components implicated in assessing force air combat capability. Two essential elements, air 
weapon system performance and force propagation potential, were positioned at the apex of the frame- 
work. They were divided into the subcomponents which define their basic dimensions. Along with the 
various categories of subsystem, the air weapon system performance group included a family of factors 
which related the subsystems in terms of configuration and combat utility. On the force propagation side 
of the ledger, inventory, mission allocation, and sortie generation subcomponents were identified. The 
importance of intangible factors such as operator proficiency and CI support was acknowledged but their 
consideration deferred to other research efforts. Each subcomponent thus identified was further divided 
into the performance attributes which contribute to its operation. These were in turn subdivided into the 
variables which describe those attributes. 

8.2 Data Collection 

The articulated analytical structure constituted the data collection matrix. While absolute validity was 
compromised by the requirements to consider only unclassified data and to estimate values for some 
unknowns, multiple sources were cross checked to develop the most accurate values possible. When data 



- 133 - 



were unavailable, they were estimated using the most accurate technique which could be supported. In 
some instances, specific data values are consequently open to challenge. While the inaccuracies are 
lamentable, they are not fatal to the evaluation technique itself and can easily be revised in subsequent 
applications. Measurement biases were minimized by closely scrutinizing observation conditions and 
adjusting reported values to a common measurement plane. Certain artiiical constraints were established 
to expedite the process. Only fixed wing aircraft with direct combat application in recent or future Middle 
Eastern combat scenarios were considered. Since the methodology aimed to support the development of 
future arms transfer policies, national air combat inventories were anchored with known data from the 
past two years and projected out to 1990. 

The final air weapon system data set consisted of performance and configuration data on 125 aircraft 
and aircraft variants, 52 target acquisition systems, 41 air-to-air missiles, and 36 aerial guns. The configu- 
ration data set mated subsystems to aircraft and addressed those performance relevant characteristics (e.g., 
navigation system) for which quantitative values were not available. A unique data set was collected to 
determine the relative utilities of attributes and subsystems in definable combat roles. A panel of 25 
fighter experts familiar with Middle Eastern air operations was polled to ascertain their views on the rela- 
tionships which obtain among attributes and subsystems in four different mission areas. The results were 
synthesized statistically and recast as relational variable values to be employed during the weapon system 
combinational phase. 

8.3 Data Aggregation 

To identify a data reduction and aggregational methodology which produced the most comprehensive 
results uninfluenced by systemic bias, off-the-shelf aggregational methodologies were evaluated to identify 
their assets and liabilities. Factor analysis stood out because of its ability to consolidate multiple variables 
into common attribute performance measures. However, its combinational logic is haphazard when 
applied at the weapon system level, and its output measures are not legitimate candidates for aggregation 
at the force level. Multi-attribute utility technique produces a judgment based combinational matrix but 
is administratively unweildly and naturally applicable only to ratio level data. The weighted linear aggre- 
gation technique developed by The Analytic Sciences Corporation incorporates expert judgment and pro- 
cesses data of any measurement level but cannot accommodate multi-variable attributes and is insensitive 
to performance variations within broadly defined subsystem categories. Whatever its strengths or weak- 
nesses, each methodology demonstrated the criticality of solid and comprehensive data input to the pro- 
duction of meaningful results. 



- 134 



Capitalizing on the strengths of existing approaches, a hybrid methodology for data reduction and 
aggregation was implemented. Factor analysis was employed to create relative index values for attributes 
described by multiple variables. Targeted at the attribute level, this minimalist version of the factor anal- 
ysis methodology purged the indices of extraneous variable influences. Ratio properties were restored to 
the indices through the utilization of a zero-valued control case the factor score for which constituted a 
threshold from which other scores in the data set could be scaled. Variables described by nominal values 
were not included in the factor problems to preclude their distorting influences but were reserved for 
introduction in the aggregation process. 

The computational phase itself was adapted with a few major variations from the linear equations 
developed by The Analytic Sciences Corporation. The process was initiated at the bottom of the analyt- 
ical ladder, combining subsystem attributes. Expert assigned values for nominally described variables were 
used to modify the raw attribute scores extracted from the data reduction phase. Attribute scores were 
combined in accordance with their relative air combat utilities in each mission area. An analogous proce- 
dure was followed at the subcomponent and component levels, with the computations not only consider- 
ing relative utility values but also conforming to specific air weapon system configurations. The product 
is a set of relative combat potential scores (Air Combat Potential Units) for each of the 125 air weapons 
systems in whatever mission roles were appropriate. 

Force propagation values were computed in a somewhat different fashion. National aircraft invento- 
ries, mission allocations, operational availability rates, maintenance requirements, and maintenance 
resources were considered in a series of equations which computed the sortie generation potential for each 
possessed air weapon system in those roles to which it would likely be committed. To illustrate the 
impact of personnel force quality on sortie generation, an additional force level factor, the relative support 
index, was also injected into selected force propagation equations. Since the variables on which the sup- 
port index was predicated are considered 'soft' surrogates for personnel quality, its general application is 
not recommended. However, its profound influence testifies to the requirement for such intangibles to be 
considered objectively or subjectively in force propagation and air combat analysis. 

In the ultimate computational step, air weapon system mission potential and national force propaga- 
tion potential were mated to produce an estimate of a country's air combat potential in four mission roles 
on a single day of flying. All of the modifying and relative utility values involved in weapon system and 
force level calculations are explicit and can be modified by the model's user to reflect differing combat 
scenarios or priorities. This feature was installed to permit user visibility and control over methodological 
functions. This model is not a black-box'. 



135 - 



8.4 Results 

The results of the aggregation phase were reviewed to determine their efficacy both at the air weapon sys- 
tem and national force levels. The results conformed to intuitive assessments and poignantly demonstrat- 
ed the desirability of employing a analytical scheme which aggregates the cumulative effects of system and 
force subcomponents on specific mission outputs. To further exercise the model, a phased analysis of a 
specific arms transfer proposal (advanced air defense fighters for Jordan) was conducted. The model 
showed itself to be responsive to the type of modifications a decision maker might stipulate in evaluating 
specific weapon system alternatives, weighing their contribution to force capabilities under varying condi- 
tions, and analyzing their impact on regional military balances under differing conflict scenarios. 

8.5 Evaluation 

The air combat potential aggregation methodology proposed in this study is a powerful and flexible 
mechanism with which to analyze the composition, benefits, and liabilities of air weapon systems transfers 
individually and at the force and regional levels. However, the methodology is far from perfect possessing 
some drawbacks which are easily surmountable and others which might prove impervious to systematic 
solution. The most prominent strengths and weakness of the of the proposed model, arranged according 
to study criteria, are outlined below. 

• Throughout, the focus on mission relevant combat output was maintained. However, the linear 
combinational form and the absence of key combat related intangibles produce results which are 
static indicators of undepreciated potential. According to the aircrew survey, technical potential 
determines approximately 35 percent of combat effectiveness. Consequently, model output cannot 
legitimately stand alone but must be incorporated with other analysis which addresses the the 
remaining 65 percent of the question. 

• The model effectively captures the performance attributes of the most prominent aircraft subsys- 
tems and their relative combat utility under varying scenarios. In doing so, it permits the evalua- 
tion of specific configurations and subsystem alternatives. The picture could be further sharpened if 
equipment -specific quantitative values for electronic warfare equipment, air-to-ground ordnance, 
and fire control computers could be integrated. 

• Methodological output is composed of ratio level measurements which can be aggregated into a 
virtually infinite variety of combinations to permit comparisons across any spectrum. However, the 
measurements are not absolute and are relevant only in relation to other values derived from the 
same data set and analytical model. 



- 136 



• The data reduction and aggregation methodology is transparent and free of crippling systemic bias. 
Two drawbacks are the requirement to reprocess data sets statistically to determine new relative 
attribute values as systems are added to the data set and the linear computational form noted in an 
earlier comment. 

• Methological assumptions and limitations were underscored throughout the discussion. The more 
important assumptions are represented mathmatically in the computational equations and can be 
modified to accommodate revised assumptions or priorities. Given the prototype's processing 
environment, making these adjustments is at present a decidely complicated and user- unfriendly' 
task. 

8.6 Suggestion for Further Development 

The methodology's underlying philosphy, analytical framework, and combinational scheme are valid and 
extendable to other regions, categories of weapons, and analytical problems. But first some enhancements 
are required to shore up its validity and applicability. 

• A classified data base should be created and expanded to include additional aircraft, subsytems, and 
regions. This process would obviate inaccuracies and permit application to other Thrid World 
regions. 

• Analytical subsets addressing elements of the ground air defense environment could also be intro- 
duced into the model relatively painlessly to permit analysis of a complete air combat picture. 

• A microcomputer based version of the analytical methodology should be developed permitting 
direct user interaction. The feasibility of a menu driven micro-based system has been demonstrat- 
ed; so this objective can be readily realized given the appropriate equipment and software expertise. 

• Of greater complexity is the development of algorithms which capture the synergy among system 
and force components. One possibility is to attempt adaptation of existing air combat simulations 
to define an alternative non-linear aggregational scheme. 

• Integration of combat relevant intangibles is a similarly complex challenge. Reliable mathmatical 
representations might not prove possible, but the influences of operator proficiency and the like can 
be reasonably assessed by weapon system and regional experts and applied subjectively in inter- 
preting model output. 

8.7 Conclusion 

The air weapon system potential model is not a predictor of combat outcomes, but it does provide the 
decision maker with finely textured and responsive static indicators of individual weapon system and force 



- 137 



potential. These indicators are essential points of departure in evaluating the military dimension of secur- 
ity assistance options. With the enhancements described above, the methodology developed in this 
research effort represents a productive vehicle for intelligence community participation in the secunty 
assistance policy development process. 



- 138 



Appendix A 
FILE DESCRIPTIONS 



A.l Middle East Combat Aircraft File 

VARIABLES ON THE ACTIVE FILE 



NAME 
ACFT 
ROLE 



SPAN 

SURF 

ARWNG 

EWGT 

MWGT 

CWGT 

WLOAD 

FWGT 

FUFRAC 

MAXPWR 

TWPWR 

ASPD 

SPECENA 

PSFL100 

CSPD 

LSPD 

SPECENS 



DESCRIPTION 
AIRCRAFT NAME 

CATEGORY 

VALUE LABEL 

BMAT BOMBER- GROUND ATTACK 

FTAT FIGHTER- GROUND ATTACK 

FTTA FIGHTER/ TRAINER- GROUND ATTACK 

FTIN FIGHTER- INTERCEPTOR 

FTTI FIGHTER/ TRAINER- INTERCEPTOR 

FTMR FIGHTER-MULTI ROLE 

FTTM FIGHTER/ TRAINER-MULTI ROLE 

FTRE FIGHTER-RECONNAISSANCE 

FTTR FIGHTER- TRAINER 

MIAT MISCELLANEOUS-GROUND ATTACK 

MITA MISCELLANEOUS /TRAINER- GROUND ATTACK 

WING SPAN (FT) 

WING SURFACE ( SQ FT) 

WING ASPECT RATIO 

EMPTY WEIGHT (LBS) 

MAXIMUM TAKEOFF WEIGHT (LBS) 

COMBAT WEIGHT (LBS) 

COMBAT WING LOADING (LBS PER SQ FT) 

INTERNAL FUEL (LBS) 

FUEL FRACTION 

MAXIMUM THRUST (LBS) 

THRUST TO WEIGHT RATIO 

MAXIMUM AIRSPEED FL3 60 (KTS) 

SPECIFIC ENERGY AT ALTITUDE (FPS) 

EST SPECIFIC EXCESS POWER FL100 M. 9 

CLIMB SPEED SEA LEVEL (FPM) 

MAXIMUM AIRSPEED SEA LEVEL (KTS) 

SPECIFIC ENERGY AT SEA LEVEL (FPS) 

- 139- 



SSPD STALL SPEED (KTS) 

LIMG COMBAT G LIMIT 

TURATE EST TURN RATE AT SL ( DEG PER SEC) 

SCEIL SERVICE CEILING (FT) 

FRANGE FERRY RANGE (NM) 

CRANGE COMBAT RANGE (NM) 

AIRAD AIR INTERCEPT RADIUS (NM) 

GARAD GROUND ATTACK RADIUS (NM) 

NGUN NUMBER OF INTERNAL GUNS 

CAL CALIBRE OF GUN(S) 

ROUNDS ROUNDS GUN ORDNANCE 

STNS NUMBER OF WEAPON STATIONS 

MAXORD MAXIMUM ORDNANCE (LBS) 

VGW VARIABLE GEOMETRY WING 
VALUE LABEL 
1 YES 

NO 

VCW VARIABLE CAMBER WING 
VALUE LABEL 

1 YES 
NO 



140 - 



A.2 Middle East Target Acquisition System File 

VARIABLES ON THE ACTIVE FILE 
DESCRIPTION 
EQUIPMENT NAME 



NAME 
NAME 
CODE 



PWR 

CONE 

UPRNG 

DWNRNG 

DATAPTS 

TWS 



ILLUM 



MAP 



DBS 



ECCM 



EQUIPMENT TYPE 

VALUE LABEL 

IRAI IR SEARCH- TRACK 

LAGA GROUND ATTACK LASER 

RAAI AIR INTERCEPT RADAR 

RAGA GROUND ATTACK RADAR 

RAMU MULTI-PURPOSE RADAR 

OUTPUT POWER (KW) 

SEARCH AZIMUTH (DEG) 

RANGE -CO OR HI ALT TGT (NM) 

RANGE -LO ALT TGT (NM) 

DATA POINTS REPORTED 

TRACK WHILE SCAN 
VALUE LABEL 

NO 

1 YES 

CW ILLUMINATION 
VALUE LABEL 

NO 

1 YES 

GROUND MAPPING 
VALUE LABEL 

NO 

1 YES 

DOPPLER BEAM SHARPENING 
VALUE LABEL 

NO 

1 YES 

ECM SUSCEPTIBILITY RATING 
VALUE LABEL 

. 7 VERY HIGH 

. 8 HIGH 

. 9 AVERAGE 

1. LOW 

1. 1 VERY LOW 



- 141 - 



A.3 Middle East Air-to-Air Missile File 

VARIABLES ON THE ACTIVE FILE 
DESCRIPTION 
MISSILE NAME 



NAME 

MSL 

CODE 



MISSILE TYPE 

VALUE LABEL 
AAMI AIR TO AIR- INFRARED GUIDED 
AAMR AIR TO AIR- RADAR GUIDED 

PRODCC PRODUCER COUNTRY CODE 

DIAM MISSILE DIAMETER (IN) 

LENGTH MISSILE LENGTH (IN) 

MSLWGHT MISSILE WEIGHT (LBS) 

GUIDTYP TERMINAL GUIDANCE MODE 

VALUE LABEL 

ARH ACTIVE RADAR 

CG COMMAND GUIDED 

EO ELECTRO OPTICAL 

IR INFRARED 

LASR LASER GUIDED 

SARH SEMIACTIVE RADAR 

GUIDSC GUIDANCE SCORE 

WHWGHT WARHEAD WEIGHT (LBS) 

FUZE NUMBER FUZE OPTIONS 

MAXHRNG MAXIMUM HEAD-ON RANGE (NM) 

MINHRNG MINIMUM HEAD-ON RANGE (NM) 

MAXTRNG MAXIMUM TAIL- CHASE RANGE (NM) 

MINTRNG MINIMUM TAIL- CHASE RANGE (NM) 

MSPD MAXIMUM SPEED (MACH) 

LIMG G LIMITATION 

ECCM ECM SUSCEPTIBILITY 

VALUE LABEL 

. 7 VERY LOW 

. 8 LOW 

. 9 AVERAGE 

1. HIGH 

1. 1 VERY HIGH 

EFFHRNG EFFECTIVE HEAD-ON RANGE 

EFFTRNG EFFECTIVE TAIL- CHASE RANGE 

MODE MISSILE LOCK-ON MODE 
VALUE LABEL 

VR VISUAL RANGE ONLY 
BVR BEYOND VISUAL RANGE 



- 142 



GIUDADX GUIDANCE SCORE AIR DEFENSE 
GUIDAS GUIDANCE SCORE AIR SUPERIORITY 



143- 



A.4 Middle East Aerial Gun File 

VARIABLES ON THE ACTIVE FILE 
DESCRIPTION 
GUN DESIGNATOR 



NAME 

GUN 

CODE 



GUN TYPE 

VALUE LABEL 
AAAG ANTI-AIRCRAFT GUN 
ACCE ACFT CANNON EXTERNAL 
ACCI ACFT CANNON INTERNAL 

PRODCC PRODUCER COUNTRY CODE 

CAL CALIBRE (MM) 

MRNG MAXIMUM EFFECTIVE RANGE (NM) 

DISP DISPERSION (MILS) 

MVEL MUZZLE VELOCITY (FPS) 

RATE MAXIMUM RATE OF FIRE ( SPM) 



- 144 - 



A.5 Middle East Air Weapon System Configuration File 

VARIABLES ON THE ACTIVE FILE 
DESCRIPTION 
AIRCRAFT NAME 
AIRCRAFT TYPE 
PRODUCER COUNTRY CODE 
CREWMEMBERS 



NAME 

ACFT 

CODE 

PRODCC 

CREW 

ARC 



AIR REFUELING CAPABLE 
VALUE LABEL 

NO 

1 YES 

NAVCAT NAVIGATION CATEGORY 

VALUE LABEL 

DOP DOPPLER NAV SYSTEM 

DR DEAD RECKONING 

GPS GLOBAL POSITIONING SYSTEM 

INS INERTIAL NAV SYSTEM 

TAC TACAN TYPE SYSTEM 

RWR RADAR WARNING RECEIVER 
VALUE LABEL 

NO 

1 YES 

PECM PASSIVE ELECTRONIC COUNTERMEASURES 
VALUE LABEL 

NO 

1 YES 

AECM ACTIVE ELECTRONIC COUNTERMEASURES 
VALUE LABEL 

NO 

1 YES 

AAMR PRIMARY RADAR AAM 

NAAMR NUMBER RADAR AAM 

AAMI PRIMARY IR AAM 

NAAMI NUMBER IR AAM 

GUN INTERNAL GUN 

PGMC PRECISION GUIDED MUNITIONS CARRIER 
VALUE LABEL 

NO 

1 YES 

SA STABILITY AUGMENTATION 
VALUE LABEL 

NO 

1 YES 

HUD HEAD UP DISPLAY 
VALUE LABEL 

NO 

1 YES 

CRP RELEASE POINT COMPUTER 

- 145 - 



VALUE LABEL 

NO 

1 YES 

TARAD RADAR TGT ACQ SYSTEM 

TAOTH SECONDARY TGT ACQ SYSTEM 

MMHFH MAN MAINTENANCE HOURS PER FLYING HOUR 



- 146 



A.6 Middle East Air Order of Battle 1984-1990 

VARIABLES ON THE ACTIVE FILE 



NAME 
CC 



ACFT 
EMCODE 



INV84 
INV85 
INV86 
INV87 
INV88 
INV89 
INV90 
MXRAT 
OAR 



DESCRIPTION 

COUNTRY CODE 

VALUE LABEL 

AG ALGERIA 

BA BAHRAIN 

EG EGYPT 

ET ETHIOPIA 

IR IRAN 

IS ISRAEL 

IZ IRAQ 

JO JORDAN 

KU KUWAIT 

LE LEBANON 

LY LIBYA 

MO MOROCCO 

MU OMAN 

QA QATAR 

SA SAUDI ARABIA 

SO SOMALIA 

SU SUDAN 

SY SYRIA 

TC UNITED ARAB EMIRATES 

TS TUNISIA 

YE NORTH YEMEN 

YS SOUTH YEMEN 

AIRCRAFT NAME 

LIKELY EMPLOYMENT ROLE 

VALUE LABEL 

BMR BOMBER 

CIN COUNTER- INSURGENCY 

FGA FIGHTER- GROUND ATTACK 

FIN FIGHTER- INTERCEPTOR 

FMR FIGHTER-MULTI ROLE 

OCA OPNL CONVERSION-AIR-TO-AIR 

OCG OPNL CONVERSION- GROUND ATTACK 

OCM OPNL CONVERSION-MULTIROLE 

REC RECONNAISSANCE 

TNG TRAINING 

1984 INVENTORY 

1985 INVENTORY 

1986 INVENTORY 
198 7 INVENTORY 

1988 INVENTORY 

1989 INVENTORY 

1990 INVENTORY 
MAINTENANCE MAN/ACFT RATIO 
OPERATIONALLY AVAILABLE RATE 



147 



Appendix B 
MIDDLE EAST AIR WEAPON SYSTEMS DATA 



B. 1 Airframes 



ACFT 



ROLE 



ALPHAMS1 


FTTC 


ALPHAMS2 


FTAT 


AMX 


FTAT 


A10A 


FTAT 


A37B 


FTAT 


A4H 


FTAT 


A4KU 


FTAT 


A4N 


FTAT 


A7E 


FTAT 


A7P 


FTAT 


BAC167 


FTAT 


CM170 


FTTC 


CM170I 


FTTC 


C101BB 


FTAT 


C101CC 


FTAT 


C101DD 


FTTA 


FA18L 


FTMR 


F104GCF 


FTAT 


F14AC 


FTIN 


F15A 


FTMR 


F15B 


FTTM 


F15C 


FTMR 


F15CFP 


FTMR 


F15D 


FTTM 


F15E 


FTMR 


F16A 


FTMR 


F16B 


FTTM 


F16C 


FTMR 


F16CSC 


FTMR 


F16D 


FTTM 


F16J79 


FTMR 


F20 


FTMR 


F20A 


FTMR 


F4CD 


FTMR 


F4EF 


FTMR 


F4M0D 


FTMR 


F5A 


FTMR 


F5B 


FTMR 


F5E 


FTMR 


F5F 


FTMR 


F86F 


FTMR 


G91Y 


FTAT 


HARMK80 


FTMR 


HAWK200 


FTMR 


HAWK50T 


FTTA 


HAWK60A 


FTAT 


HAWK60T 


FTTA 


HUNTER 


FTMR 


HUNTERT 


FTTM 


IL28 


BMAT 


JAGI04 


FTAT 



SPAN 



SURF 



ARWNG 



EWGT 



FWGT 



CWGT 



30 


188 


4. 


75 


7374 


3351 


11805 


30 


188 


4. 


75 


7749 


3648 


12328 


29 


266 


3. 


18 


13228 


4409 


19621 


58 


506 


6. 


53 


21541 


10700 


34062 


36 


184 


7. 


01 


6211 


3448 


10775 


28 


260 


2. 


91 


10100 


5440 


17120 


28 


260 


2. 


91 


10100 


5440 


17120 


28 


260 


2. 


91 


10800 


5440 


18255 


39 


375 


4. 


01 


19127 


10200 


31727 


39 


375 


4. 


01 


19781 


10200 


32006 


35 


214 


5. 


83 


6195 


2203 


8797 


40 


186 


8. 


51 


5093 


1754 


6135 


40 


186 


8. 


51 


5093 


1754 


6135 


35 


215 


5, 


62 


7606 


4260 


12216 


35 


215 


5. 


62 


7606 


4260 


12216 


35 


215 


5. 


62 


7606 


4260 


12216 


38 


400 


3. 


52 


20860 


10380 


27432 


22 


196 


2. 


47 


14082 


5819 


20742 


38 


565 


2. 


58 


39921 


16200 


50335 


43 


608 


3. 


01 


28000 


11635 


37212 


43 


608 


3. 


01 


28800 


11635 


38012 


43 


608 


3. 


01 


28000 


13455 


38122 


43 


608 


3. 


01 


28000 


23205 


43001 


43 


608 


3. 


01 


28800 


13455 


38922 


43 


608 


3. 


01 


28000 


13455 


37064 


31 


300 


3. 


20 


15586 


6972 


19824 


31 


300 


3. 


20 


16258 


5787 


19904 


31 


300 


3. 


20 


18259 


6972 


23127 


31 


300 


3. 


20 


18259 


6972 


22433 


31 


300 


3. 


20 


19059 


6972 


23927 


31 


300 


3. 


20 


17780 


6972 


21954 


27 


186 


3. 


86 


11220 


5050 


14433 


27 


186 


3. 


86 


11220 


5050 


15127 


38 


530 


2. 


78 


28000 


15614 


37101 


39 


530 


2. 


80 


30328 


15630 


39525 


39 


530 


2. 


80 


30328 


20094 


41761 


25 


170 


3. 


77 


8085 


3166 


10012 


25 


170 


3. 


77 


8361 


3116 


10263 


27 


186 


3. 


83 


9723 


4063 


12099 


27 


186 


3. 


83 


10576 


4603 


13222 


37 


288 


4. 


78 


10950 


3910 


12905 


30 


195 


4. 


46 


8598 


3736 


12466 


25 


201 


3. 


18 


13000 


5060 


16282 


31 


180 


5. 


28 


8750 


3000 


10626 


31 


180 


5. 


28 


8015 


3060 


10315 


31 


180 


5. 


28 


8015 


3060 


12945 


31 


180 


5. 


28 


8015 


3060 


10315 


34 


349 


3. 


25 


13270 


3199 


14870 


34 


349 


3. 


25 


14070 


3199 


15670 


70 


655 


7. 


57 


28417 


14450 


42256 


29 


260 


3. 


12 


15432 


7540 


24452 



37! 



148 



JAGI11 


FTAT 


29 


260 


3. 


12 


15432 


7540 


24452 


34612 


JASTREB 


FTAT 


34 


209 


5. 


66 


6217 


2600 


8727 


11243 


KFIRC2 


FTMR 


27 


375 


1. 


95 


16060 


5670 


19715 


35715 


KFIRC7 


FTMR 


27 


375 


1. 


95 


16060 


5670 


19695 


35715 


KFIRTC2 


FTTM 


27 


375 


1. 


95 


16860 


5670 


20105 


35715 


LAVI 


FTMR 


29 


350 


2. 


34 


15500 


6000 


19300 


37500 


LIGHTNG 


FTIN 


35 


380 


3. 


19 


28000 


12000 


34660 


50000 


L29 


FTTA 


40 


283 


5. 


58 


5027 


1905 


6200 


7804 


L39ZA 


FTTC 


31 


202 


4. 


75 


8060 


2122 


10334 


12346 


MB326K 


FTAT 


33 


208 


5. 


24 


5907 


1568 


8691 


11475 


MB326L 


FTTA 


33 


208 


5. 


24 


5907 


1568 


8691 


11475 


MB339A 


FTTC 


36 


208 


6. 


10 


6889 


2425 


10102 


13000 


MB339C 


FTAT 


36 


208 


6. 


31 


7066 


3523 


10963 


13558 


MB339K 


FTAT 


36 


208 


6. 


31 


7066 


3523 


10963 


13558 


MIG15BIS 


FTMR 


33 


255 


4. 


27 


8115 


2586 


9408 


11085 


MIG15UTI 


FTTC 


33 


222 


4. 


91 


7716 


2586 


9009 


10766 


MIG17F 


FTMR 


36 


265 


4. 


89 


9220 


2962 


10701 


13393 


MIG19C 


FTMR 


30 


269 


3. 


41 


12700 


3721 


14941 


20062 


MIG21C 


FTIN 


24 


248 


2. 


25 


12440 


4202 


15301 


19026 


MIG21F 


FTMR 


24 


248 


2. 


25 


12300 


4300 


15210 


20723 


MIG21JKL 


FTMR 


24 


248 


2. 


25 


12300 


4668 


15394 


20723 


MIG21R 


FTRE 


24 


248 


2. 


25 


12440 


4300 


15590 


20863 


MIG21UM 


FTTM 


24 


248 


2. 


25 


13100 


4300 


16010 


21853 


MIG23B 


FTMR 


47 


400 


5. 


47 


21250 


12168 


30064 


41670 


MIG23E 


FTMR 


47 


400 


5. 


47 


21200 


12168 


28044 


44312 


MIG23F 


FTAT 


47 


401 


5. 


46 


24250 


12168 


32534 


44312 


MIG23G 


FTMR 


47 


400 


5. 


48 


21450 


12168 


29186 


41670 


MIG23UM 


FTTC 


47 


400 


5. 


47 


22000 


10300 


29350 


41000 


MIG25 


FTIN 


46 


612 


3. 


43 


44100 


27000 


63860 


79800 


MIG25R 


FTRE 


44 


603 


3. 


21 


43200 


27000 


60700 


73635 


MIG25U 


FTTI 


46 


612 


3. 


43 


44090 


27000 


63850 


79800 


MIG27DJ 


FTAT 


47 


401 


5. 


46 


23787 


12168 


33179 


39685 


MIG29 


FTMR 


34 


380 


3. 


11 


25000 


8800 


32242 


37500 


MIG31 


FTIN 


44 


580 


3. 


40 


48115 


27000 


64457 


90725 


MIRF1A 


FTMR 


28 


269 


2. 


81 


16314 


7379 


21710 


32850 


MIRF1B 


FTTI 


28 


269 


2. 


81 


16314 


7379 


21710 


32850 


MIRF1C 


FTMR 


28 


269 


2. 


81 


16314 


7379 


21502 


32850 


MIRF1E 


FTMR 


28 


269 


2. 


81 


17857 


7379 


23045 


33510 


MIRIIIC 


FTIN 


27 


375 


1. 


94 


13570 


5039 


17789 


17637 


MIRIIIE 


FTMR 


27 


375 


1. 


94 


14570 


5039 


18332 


17637 


MIRIIIEI 


FTMR 


27 


375 


1. 


94 


14570 


5039 


18346 


17637 


MIR2000C 


FTMR 


30 


441 


1. 


97 


16535 


6513 


21188 


36375 


MIR2000R 


FTRE 


30 


441 


1. 


97 


16535 


5860 


20090 


36375 


MIR2000T 


FTTM 


30 


441 


1. 


97 


17235 


6513 


21888 


36375 


MIR3NG 


FTMR 


27 


375 


1. 


94 


17000 


5959 


21478 


32400 


MIR4000 


FTMR 


39 


786 


1. 


98 


24220 


19539 


35386 


unk 


MIR5DD 


FTTA 


27 


375 


1. 


94 


15350 


5842 


22271 


30200 


MIR5DR 


FTRE 


27 


375 


1. 


94 


14550 


5842 


21880 


30200 


MIR5D1 


FTIN 


27 


375 


1. 


94 


14550 


5842 


18714 


30200 


MIR5D1E 


FTIN 


27 


375 


1. 


94 


14550 


5842 


18867 


30200 


MIR5D2 


FTAT 


27 


375 


1. 


94 


14550 


5842 


22101 


30200 


OV10D 


MIAT 


40 


291 


5. 


50 


6893 


1714 


9550 


14444 


PRCA5 


FTAT 


32 


301 


3. 


36 


14317 


6356 


19700 


26455 


PRCFT6 


FTTM 


30 


269 


3. 


39 


12700 


3432 


14416 


22045 


PRCF6 


FTMR 


30 


269 


3. 


39 


12700 


3725 


14943 


22045 


PRCF7 


FTIN 


24 


248 


2. 


25 


12440 


4202 


15301 


19026 


PRCF7E 


FTIN 


24 


248 


2. 


25 


12440 


4202 


15265 


19026 


RF4C 


FTRE 


38 


530 


2. 


78 


29000 


15164 


36782 


58000 


RF5E 


FTRE 


27 


186 


3. 


83 


10723 


4603 


13225 


24722 


SF260MW 


MITA 


27 


109 


6. 


91 


1830 


373 


2347 


2866 


SF260TP 


MITA 


27 


109 


6. 


91 


1654 


403 


2186 


2366 


SUPETEN 


FTAT 


32 


306 


3. 


27 


14220 


5428 


19249 


19259 


SU20 


FTAT 


46 


432 


4. 


90 


22050 


8157 


30539 


39020 


SU22 


FTAT 


46 


432 


4. 


90 


22500 


8580 


32302 


42330 


SU25 


FTAT 


51 


450 


5. 


73 


17250 


10000 


26660 


36050 


SU2 7 


FTMR 


48 


500 


4. 


51 


39000 


15500 


48948 


63500 


SU7BMKL 


FTAT 


29 


297 


2. 


89 


19040 


5181 


24381 


29750 


SU7U 


FTTA 


29 


297 


2. 


89 


19000 


5181 


24341 


29750 



- 149- 



TA4EH 


FTTA 


28 


260 


2. 


91 


10084 


5440 


TA4KU 


FTTA 


28 


260 


2. 


91 


10900 


5440 


TORADV 


FTIN 


46 


400 


5. 


20 


31500 


15632 


TORIDS 


FTAT 


46 


400 


5. 


20 


31065 


14000 


TU16AG 


BMAT 


108 


1772 


6. 


58 


82000 


56870 


TU22BD 


BMAT 


91 


1451 


5. 


69 


80400 


81600 



16904 237 

17720 245 

41392 60C 

47985 60C 



- 150 



ACFT 


MAXPWR 


TWPWR 


ASPD 


SPECENA 


LSPD 


SPECENS 


CSPD 


SCE1 


ALPHAMS1 


5952 


.50 


487 


975. 


30 


540 


215. 


54 


11220 


480C 


ALPHAMS2 


5952 


.48 


487 


975. 


30 


540 


215. 


54 


11218 


480C 


AMX 


11030 


.56 


700 


1195. 


52 


628 


291. 


51 


15000 


500C 


A10A 


18130 


. 53 


450 


716. 


35 


380 


106. 


73 


6000 


340C 


A37B 


5700 


.53 


455 


849. 


11 


403 


120. 


05 


6990 


4176 


A4H 


9300 


.54 


587 


1071. 


36 


548 


221. 


97 


8000 


490C 


A4KU 


9300 


.54 


561 


1032. 


63 


548 


221. 


97 


8000 


480C 


A4N 


11200 


. 61 


583 


1067. 


90 


560 


231. 


80 


10300 


490C 


A7E 


15000 


.47 


720 


974. 


85 


600 


266. 


10 


20000 


355C 


A7P 


12200 


. 38 


563 


825. 


96 


600 


266. 


10 


12000 


355C 


BAC167 


3410 


. 39 


410 


791. 


19 


391 


113. 


00 


5250 


4001 


CM170 


2116 


. 34 


392 


613. 


58 


378 


105. 


61 


3740 


300C 


CM170I 


2116 


.34 


392 


613. 


58 


378 


105. 


61 


3740 


300C 


C101BB 


3700 


. 30 


430 


803. 


34 


373 


102. 


84 


3780 


400C 


C101CC 


4700 


.38 


450 


849. 


68 


373 


102. 


84 


5300 


420C 


C101DD 


4700 


. 38 


450 


849. 


68 


373 


102. 


84 


5300 


420C 


FA18L 


32000 


1. 17 


1146 


1887. 


41 


730 


393. 


90 


60000 


5501 


F104GCF 


15800 


. 76 


1232 


2088. 


58 


690 


351. 


91 


50000 


580C 


F14AC 


41800 


. 83 


1342 


2264. 


53 


702 


364. 


26 


30000 


560C 


F15A 


47860 


1. 29 


1433 


2601. 


18 


700 


362. 


19 


50000 


650C 


F15B 


47860 


1.26 


1433 


2601. 


18 


700 


362. 


19 


50000 


650C 


F15C 


47860 


1.26 


1433 


2601. 


18 


700 


362. 


19 


50000 


650C 


F15CFP 


47860 


1. 11 


1433 


2601. 


18 


650 


312. 


29 


29000 


650C 


F15D 


47860 


1.23 


1433 


2601. 


18 


700 


362. 


19 


50000 


650C 


F15E 


54820 


1.48 


1433 


2601. 


18 


670 


331. 


81 


50000 


650C 


F16A 


25000 


1.26 


1175 


1853. 


83 


793 


464. 


82 


50000 


500C 


F16B 


25000 


1.26 


1175 


1853. 


83 


793 


464. 


82 


50000 


500C 


F16C 


25000 


1.08 


1175 


1853. 


83 


793 


464. 


82 


50000 


500C 


F16CSC 


25000 


1. 11 


1175 


1853. 


83 


793 


464. 


82 


50000 


500C 


F16D 


25000 


1.04 


1175 


1853. 


83 


793 


464. 


82 


50000 


500C 


F16J79 


18000 


.82 


1146 


1804. 


08 


687 


348. 


86 


50000 


500C 


F20 


17000 


1. 18 


1146 


1887. 


41 


694 


356. 


00 


52800 


550C 


F20A 


17000 


1. 12 


1146 


1887. 


41 


694 


356. 


00 


52800 


550C 


F4CD 


34000 


. 92 


1275 


2201. 


59 


773 


441. 


67 


28000 


600C 


F4EF 


35800 


. 91 


1301 


2230. 


26 


787 


457. 


81 


28000 


5875 


F4M0D 


41200 


. 99 


1301 


2230. 


26 


787 


457. 


81 


28000 


58 7 5 


F5A 


8160 


.82 


802 


1325. 


43 


635 


298. 


05 


28700 


5 IOC 


F5B 


8160 


. 80 


768 


1285. 


97 


635 


298. 


05 


28700 


5 IOC 


F5E 


10000 


. 83 


934 


1508. 


14 


661 


322. 


95 


34500 


518C 


F5F 


10000 


. 76 


894 


1440. 


76 


661 


322. 


95 


32890 


510C 


F86F 


5970 


.46 


670 


1215. 


14 


650 


312. 


29 


17700 


530C 


G91Y 


8160 


. 65 


544 


902. 


08 


600 


266. 


10 


17000 


4 IOC 


HARMK80 


21500 


1. 32 


739 


1257. 


00 


641 


303. 


71 


20000 


5120 


HAWK200 


5700 


.54 


688 


1183. 


21 


560 


231. 


80 


1200 


5000 


HAWK50T 


5340 


.52 


575 


1077. 


72 


535 


211. 


57 


11800 


5000 


HAWK60A 


5700 


.44 


575 


1077. 


72 


560 


231. 


80 


11800 


5000 


HAWK60T 


5700 


.55 


575 


1077. 


72 


560 


231. 


80 


11800 


5000 


HUNTER 


10000 


.67 


622 


1202. 


63 


621 


285. 


05 


17500 


5500 


HUNTERT 


10000 


. 64 


622 


1202. 


63 


621 


285. 


05 


17500 


5500 


IL28 


11904 


.28 


434 


811. 


72 


432 


137. 


94 


2952 


4035 


JAGI04 


16800 


. 69 


917 


1621. 


55 


729 


392. 


82 


26100 


600C 


JAGI11 


18540 


. 76 


917 


1621. 


55 


729 


392. 


82 


28000 


6000 


JASTREB 


3000 


. 34 


422 


787. 


88 


408 


123. 


04 


4135 


3937 


KFIRC2 


17900 


. 91 


1317 


2248. 


73 


750 


415. 


78 


45930 


5800 


KFIRC7 


18900 


. 96 


1317 


2248. 


73 


750 


415. 


78 


45930 


5800 


KFIRTC2 


17900 


.89 


1317 


2248. 


73 


750 


415. 


78 


45930 


5800 


LAV I 


20620 


1. 07 


1060 


1797. 


18 


597 


263. 


44 


30900 


5800 


LIGHTNG 


32600 


. 94 


1318 


2284. 


01 


700 


362. 


19 


50000 


6000 


L29 


1960 


. 32 


353 


592. 


11 


332 


81. 


47 


2755 


3000 


L39ZA 


3792 


. 37 


373 


704. 


50 


340 


85. 


45 


4130 


3610 


MB326K 


3360 


. 39 


470 


813. 


81 


460 


156. 


41 


6494 


3903 


MB326L 


3360 


. 39 


470 


813. 


81 


460 


156. 


41 


6494 


3903 


MB339A 


4000 


.40 


441 


943. 


75 


485 


173. 


87 


6595 


4800 


MB339C 


4450 


.41 


441 


902. 


09 


490 


177. 


47 


6550 


4550 


MB339K 


4450 


.41 


441 


902. 


09 


490 


177. 


47 


6550 


45 5 


MIG15BIS 


5952 


. 63 


582 


1097. 


92 


567 


237. 


63 


10400 


5085 



- 151 - 






MIG15UTI 


5450 


. 60 


565 


MIG17F 


7400 


. 69 


570 


MIG19C 


14200 


. 95 


779 


MIG21C 


12677 


. 83 


1031 


MIG21F 


13688 


. 90 


1159 


MIG21JKL 


14550 


. 95 


1177 


MIG21R 


13688 


. 88 


1159 


MIG21UM 


14550 


. 91 


1177 


MIG23B 


27350 


. 91 


1290 


MIG23E 


27350 


. 98 


1290 


MIG23F 


23350 


. 72 


974 


MIG23G 


27500 


. 94 


1318 


MIG23UM 


22485 


. 77 


1280 


MIG25 


50020 


. 78 


1616 


MIG25R 


50020 


.82 


1616 


MIG25U 


50020 


. 78 


1616 


MIG27DJ 


25350 


. 76 


974 


MIG29 


38000 


1. 18 


1318 


MIG31 


61730 


. 96 


1500 


MIRF1A 


15873 


. 73 


1261 


MIRF1B 


15873 


. 73 


1261 


MIRF1C 


15873 


. 74 


1261 


MIRF1E 


15873 


. 69 


1433 


MIRIIIC 


13225 


. 74 


1261 


MIRIIIE 


13670 


. 75 


1261 


MIRIIIEI 


13670 


. 75 


1261 


MIR2000C 


19840 


. 94 


1347 


MIR2000R 


19840 


. 99 


1318 


MIR2000T 


19840 


. 91 


1347 


MIR3NG 


15873 


. 74 


1261 


MIR4000 


42770 


1.21 


1318 


MIR5DD 


13670 


. 61 


1261 


MIR5DR 


13670 


. 62 


1261 


MIR5D1 


13670 


. 73 


1270 


MIR5D1E 


13670 


. 72 


1270 


MIR5D2 


13670 


. 62 


1261 


OV10D 


2500 


.26 


250 


PRCA5 


14330 


. 73 


774 


PRCFT6 


14330 


. 99 


720 


PRCF6 


14330 


. 96 


720 


PRCF7 


12677 


.83 


1031 


PRCF7E 


12677 


.83 


1031 


RF4C 


34000 


. 92 


1275 


RF5E 


10000 


. 76 


894 


SF260MW 


475 


. 20 


235 


SF260TP 


505 


. 23 


235 


SUPETEN 


11265 


.59 


573 


SU20 


24700 


.81 


1220 


SU22 


25350 


. 78 


1220 


SU25 


18000 


. 68 


475 


SU27 


60000 


1. 23 


1350 


SU7BMKL 


19841 


.81 


896 


SU7U 


19841 


.82 


896 


TA4EH 


8500 


. 50 


596 


TA4KU 


9300 


. 52 


561 


TORADV 


33600 


.81 


1301 


TORIDS 


32000 


. 67 


1261 


TU16AG 


41900 


. 32 


535 


TU22BD 


61800 


.42 


800 



1085. 

1147. 

1427. 

1742. 

1949. 

1980. 

1949. 

1980. 

2246. 

2230. 

1701. 

2284. 

2227. 

3263. 

3406. 

3263. 

1576. 

2367. 

2746. 

2268. 

2268. 

2268. 

2680. 

2104. 

2104. 

2104. . 

2324.47 

2267. 34 

2324. 

2075. 

2377. 

2104. 

2104. 

2121. 

2121. 

2104. 

546. 
1276. 
1361. 
1258. 
1742. 
1742. 
2180. 
1454. 

285. 

507. 

992. 
2084. 
2084. 

750. 
2347. 
1421. 
1421. 
1079. 
1032. 
2084. 
2008. 

884. 
1473. 



96 
82 
30 
36 
56 
64 
56 
64 
70 
03 
22 
01 
70 
61 
61 
61 
22 
34 
44 
68 
68 
68 
35 
93 
93 
93 



47 
35 
34 
93 
93 
77 
77 
93 
20 
14 
93 
06 
36 
36 
76 
09 
82 
49 
69 
33 
33 
11 
11 
74 
74 
23 
63 
43 
68 
07 
06 



549 
545 
628 
600 
650 
680 
650 
675 
727 
727 
629 
727 
661 
650 
650 
650 
629 
793 
750 
693 
693 
693 
793 
734 
754 
754 
793 
777 
793 
734 
600 
800 
800 
800 
800 
800 
250 
721 
641 
641 
535 
535 
773 
661 
165 
216 
648 
680 
680 
380 
725 
450 
450 
550 
548 
793 
782 
530 
600 



222. 
219. 
291. 
266. 
312. 
341. 
312. 
336. 
390. 
390. 
292. 
390. 
322. 
312. 
312. 
312. 
292. 
464. 
415. 
354. 
354. 
354. 
464. 
398. 
420. 
420. 
464. 
446. 
464. 
398. 
266. 
473. 
473. 
473. 
473. 
473. 

46. 
384. 
303. 
303. 
211. 
211. 
441. 
322. 

20. 

34. 
310. 
341. 
341. 
106. 
388. 
149. 
149. 
223. 
221. 
464. 
452. 
207. 
266. 



78 

55 
51 
10 
29 
79 
29 
78 
67 
67 
44 
67 
95 
29 
29 
29 
44 
82 
78 
98 
98 
98 
82 
22 
22 
22 
82 
25 
82 
22 
10 
06 
06 
06 
06 
06 
20 
24 
71 
71 
57 
57 
67 
95 
12 
49 
37 
79 
79 
73 
52 
68 
68 
59 
97 
82 
01 
63 
10 



10400 
8000 
15000 
21000 
25900 
30000 
25900 
30000 
50000 
50000 
50000 
50000 
50000 
40950 
40950 
40950 
40000 
50000 
45000 
47835 
47835 
47835 
59000 
16400 
16400 
16400 
49000 
47429 
49000 
20000 
65600 
16400 
16400 
16400 
16400 
16400 
3020 
15000 
30000 
30000 
21000 
21000 
28000 
34500 
1250 
2170 
24600 
45275 
45275 
6500 
50000 
29500 
29900 
8440 
8000 
30000 
30000 
13100 
22100 



- 152 



ACFT 


LIMG 


WLOAD 


TURATE 


PSFL100 


SSPD 


ALPHAMS1 


9. 


00 


62. 


66 


21. 


79 


175. 


86 


116 


ALPHAMS2 


9. 


00 


65. 


44 


21. 


76 


163. 


64 


116 


AMX 


7. 


33 


73. 


76 


17. 


76 


243. 


53 


90 


A10A 


7. 


33 


67. 


32 


20. 


86 


208. 


15 


unk 


A37B 


7. 


33 


58. 


59 


17. 


72 


168. 


65 


75 


A4H 


7. 


33 


65. 


85 


17. 


74 


193. 


08 


unk 


A4KU 


7. 


33 


65. 


85 


17. 


74 


193. 


08 


unk 


A4N 


7. 


33 


70. 


21 


17. 


82 


252. 


49 


unk 


A7E 


6. 


50 


84. 


61 


15. 


62 


183. 


59 


unk 


A7P 


6. 


50 


85. 


35 


15. 


52 


108. 


45 


unk 


BAC167 


6. 


00 


41. 


16 


16. 


52 


10. 


06 


99 


CM170 


7. 


33 


32. 


97 


20. 


97 


-66. 


22 


unk 


CM170I 


7. 


33 


32. 


97 


20. 


97 


-66. 


22 


unk 


C101BB 


7. 


50 


56. 


74 


21. 


76 


-4. 


70 


88 


C101CC 


7. 


50 


56. 


74 


21. 


76 


63. 


19 


88 


C101DD 


7. 


50 


56. 


74 


21. 


76 


63. 


19 


88 


FA18L 


8. 


00 


68. 


58 


20. 


24 


736. 


43 


100 


F104GCF 


7. 


33 


105. 


77 


18. 


00 


442. 


56 


unk 


F14AC 


7. 


33 


89. 


09 


18. 


09 


485. 


08 


115 


F15A 


7. 


33 


61. 


20 


18. 


67 


821. 


32 


110 


F15B 


7. 


33 


62. 


52 


18. 


63 


801. 


67 


110 


F15C 


9. 


00 


62. 


70 


22. 


95 


799. 


04 


110 


F15CFP 


9. 


00 


70. 


72 


22. 


72 


695. 


66 


110 


F15D 


9. 


00 


64. 


02 


22. 


90 


780. 


31 


110 


F15E 


9. 


00 


60. 


96 


23. 


31 


980. 


80 


110 


F16A 


9. 


00 


66. 


08 


22. 


96 


810. 


37 


unk 


F16B 


9. 


00 


66. 


34 


22. 


95 


806. 


69 


unk 


F16C 


9. 


00 


77. 


09 


22. 


67 


678. 


63 


unk 


F16CSC 


9. 


00 


74. 


78 


22. 


72 


703. 


09 


unk 


F16D 


9. 


00 


79. 


76 


22. 


61 


652. 


19 


unk 


F16J79 


9. 


00 


73. 


18 


22. 


26 


456. 


42 


unk 


F20 


9. 


00 


77. 


60 


22. 


82 


759. 


66 


unk 


F20A 


9. 


00 


81. 


33 


22. 


74 


719. 


67 


unk 


F4CD 


7. 


00 


70. 


00 


17. 


36 


531. 


41 


unk 


F4EF 


7. 


00 


74. 


58 


17. 


35 


529. 


72 


148 


F4M0D 


7. 


00 


78. 


79 


17. 


44 


602. 


60 


148 


F5A 


7. 


33 


58. 


89 


18. 


07 


425. 


33 


unk 


F5B 


7. 


33 


60. 


37 


18. 


04 


412. 


18 


unk 


F5E 


7. 


33 


65. 


05 


18. 


08 


447. 


99 


124 


F5F 


7. 


33 


71. 


08 


18. 


00 


400. 


42 


136 


F86F 


6. 


00 


44. 


82 


14. 


37 


80. 


59 


unk 


G91Y 


7. 


33 


63. 


86 


17. 


87 


303. 


04 


125 


HARMK80 


7. 


80 


80. 


96 


19. 


94 


882. 


33 


na 


HAWK200 


8. 


00 


59. 


16 


19. 


38 


194. 


89 


unk 


HAWK50T 


8. 


00 


57. 


43 


19. 


36 


175. 


20 


unk 


HAWK60A 


8. 


00 


72. 


08 


19. 


26 


139. 


90 


unk 


HAWK60T 


8. 


00 


57. 


43 


19. 


40 


204. 


14 


unk 


HUNTER 


7. 


33 


42. 


61 


17. 


89 


228. 


74 


unk 


HUNTERT 


7. 


33 


44. 


90 


17. 


85 


211. 


91 


unk 


IL28 


4. 


00 


64. 


56 


9. 


30 


-4. 


11 


119 


JAGI04 


8. 


60 


93. 


94 


21. 


07 


370. 


89 


115 


JAGI11 


8. 


60 


93. 


94 


21. 


17 


429. 


91 


115 


JASTREB 


8. 


00 


41. 


74 


19. 


13 


-19. 


13 


85 


KFIRC2 


7. 


33 


52. 


63 


18. 


18 


485. 


90 


unk 


KFIRC7 


7. 


33 


52. 


58 


18. 


25 


528. 


62 


unk 


KFIRTC2 


7. 


33 


53. 


67 


18. 


16 


474. 


30 


unk 


LAV I 


9. 


00 


55. 


17 


22. 


65 


626. 


13 


unk 


LIGHTNG 


7. 


33 


91. 


19 


18. 


22 


520. 


67 


unk 


L29 


6. 


00 


21. 


93 


19. 


45 


-188. 


71 


71 


L39ZA 


5. 


20 


51. 


05 


16. 


39 


27. 


56 


90 


MB326K 


6. 


00 


41. 


78 


14. 


30 


13. 


25 


unk 


MB326L 


6. 


00 


41. 


78 


14. 


30 


13. 


25 


unk 


MB339A 


6. 


00 


48. 


64 


14. 


31 


48. 


54 


80 


MB339C 


6. 


00 


52. 


78 


14. 


32 


69. 


97 


82 


MB339K 


6. 


00 


52. 


78 


14. 


32 


69. 


97 


82 


MIG15BIS 


6. 


50 


36. 


89 


15. 


78 


162. 


72 


113 



VCW VGW 





1 







1 
1 








1 


1 1 













1 
1 
1 
1 
1 
1 
1 
1 



1 
1 




1 
1 




1 1 

1 



1 
1 





1 
1 




1 



1 












T53 



MIG15UTI 


6. 


50 


40. 


62 


15. 


75 


160. 


43 


unk 





MIG17F 


6. 


50 


40. 


38 


15. 


85 


220. 


46 


114 





MIG19C 


8. 


00 


55. 


54 


19. 


93 


449. 


88 


unk 





MIG21C 


8. 


00 


61. 


80 


19. 


77 


398. 


72 


unk 





MIG21F 


8. 


00 


61. 


43 


19. 


87 


451. 


33 


unk 





MIG21JKL 


8. 


00 


62. 


17 


19. 


93 


486. 


52 


146 





MIG21R 


8. 


00 


62. 


96 


19. 


84 


437. 


87 


unk 





MIG21UM 


8. 


00 


64. 


66 


19. 


88 


463. 


92 


146 





MIG23B 


7. 


33 


75. 


08 


18. 


19 


480. 


38 


unk 





MIG23E 


7. 


33 


70. 


04 


18. 


27 


522. 


25 


unk 





MIG23F 


6. 


00 


81. 


13 


14. 


62 


344. 


34 


unk 





MIG23G 


7. 


33 


72. 


96 


18. 


23 


501. 


80 


unk 





MIG23UM 


7. 


33 


73. 


30 


18. 


01 


370. 


79 


unk 





MIG25 


6. 


00 


104. 


40 


14. 


69 


413. 


46 


146 





MIG25R 


6. 


00 


100. 


66 


14. 


73 


441. 


29 


146 





MIG25U 


6. 


00 


104, 


38 


14. 


69 


413. 


54 


146 





MIG27DJ 


6. 


00 


82. 


74 


14. 


67 


380. 


69 


unk 





MIG29 


9. 


00 


84. 


85 


22. 


82 


769. 


26 


unk 


1 


MIG31 


6. 


00 


111. 


13 


14. 


87 


608. 


80 


unk 





MIRF1A 


7. 


33 


80. 


68 


17. 


97 


393. 


17 


unk 


1 


MIRF1B 


7. 


33 


80. 


68 


17. 


97 


393. 


17 


unk 


1 


MIRF1C 


7. 


33 


79. 


90 


17. 


97 


398. 


07 


unk 


1 


MIRF1E 


7. 


33 


85. 


64 


17. 


91 


363. 


91 


unk 


1 


MIRIIIC 


7. 


33 


47. 


44 


17. 


98 


299. 


23 


unk 





MIRIIIE 


7. 


33 


48. 


89 


17. 


98 


305. 


51 


unk 





MIRIIIEI 


7. 


33 


48. 


92 


17. 


98 


305. 


20 


unk 





MIR2000C 


9. 


00 


48. 


04 


22. 


44 


494. 


71 


90 


1 


MIR2000R 


7. 


33 


45. 


56 


18. 


28 


527. 


85 


90 


1 


MIR2000T 


9. 


00 


49. 


63 


22. 


40 


475. 


30 


90 


1 


MIR3NG 


7. 


33 


57. 


27 


17. 


97 


358. 


40 


unk 


1 


MIR4000 


9. 


00 


45. 


02 


22. 


87 


709. 


13 


90 


1 


MIR5DD 


7. 


33 


59. 


39 


17. 


82 


233. 


63 


unk 





MIR5DR 


7. 


33 


58. 


35 


17. 


84 


239. 


61 


unk 





MIR5D1 


7. 


33 


49. 


90 


17. 


96 


297. 


22 


unk 





MIR5D1E 


7. 


33 


50. 


31 


17. 


96 


293. 


98 


unk 





MIR5D2 


7. 


33 


58. 


94 


17. 


83 


236. 


21 


unk 





OV10D 


4. 


40 


32. 


82 


18. 


71 


-122. 


09 


unk 





PRCA5 


6. 


00 


65. 


49 


14. 


63 


311. 


63 


114 





PRCFT6 


6. 


00 


53. 


57 


14. 


90 


476. 


03 


126 





PRCF6 


6. 


00 


55. 


53 


14. 


87 


536. 


35 


126 





PRCF7 


8. 


00 


61. 


80 


19. 


77 


398. 


72 


unk 





PRCF7E 


8. 


00 


61. 


65 


19. 


77 


444. 


33 


unk 





RF4C 


7. 


00 


69. 


40 


17. 


37 


536. 


99 


unk 





RF5E 


7. 


33 


71. 


10 


18. 


00 


400. 


31 


124 


1 


SF260MW 


4. 


40 


21. 


59 


28. 


35 


-322. 


22 


72 





SF260TP 


4. 


40 


20. 


11 


21. 


66 


-326. 


29 


68 





SUPETEN 


6. 


50 


62. 


97 


15. 


73 


243. 


70 


104 





SU20 


6. 


50 


70. 


76 


15. 


97 


399. 


07 


124 


1 


SU22 


6. 


50 


74. 


84 


15. 


95 


386. 


80 


124 


1 


SU25 


7. 


50 


59. 


24 


21. 


35 


279. 


14 


unk 





SU27 


9. 


00 


97. 


90 


22. 


90 


821. 


23 


unk 





SU7BMKL 


6. 


50 


82. 


09 


15. 


98 


393. 


95 


195 





SU7U 


6. 


50 


81. 


95 


15. 


98 


394. 


76 


195 





TA4EH 


7. 


33 


65. 


02 


17. 


69 


179. 


45 


unk 





TA4KU 


7. 


33 


68. 


15 


17. 


72 


203. 


47 


unk 





TORADV 


7. 


50 


103. 


48 


18. 


49 


482. 


32 


100 


1 


TORIDS 


7. 


50 


119. 


96 


18. 


31 


373. 


00 


104 


1 


TU16AG 


4. 


00 


73. 


50 


9. 


32 


37. 


16 


unk 





TU22BD 


4. 


00 


101. 


76 


9. 


38 


131. 


64 


unk 






- 154 



ACFT 


AIRAD 


GARAD 


FRANGE 


FUFRAC 


CRANGE 


ALPHAMS1 


na 


315 


2160 


. 31 


na 


ALPHAMS2 


na 


315 


2160 


. 32 


unk 


AMX 


na 


480 


1600 


.25 


unk 


A10A 


na 


300 


2131 


. 33 


unk 


A37B 


na 


216 


878 


. 36 


399 


A4H 


na 


375 


3000 


. 35 


1741 


A4KU 


na 


291 


1740 


. 35 


unk 


A4N 


na 


355 


1788 


. 33 


800 


A7E 


na 


622 


2431 


. 35 


unk 


A7P 


na 


622 


2431 


. 34 


unk 


BAC167 


na 


255 


1404 


.26 


630 


CM170 


na 


251 


755 


.26 


unk 


CM170I 


na 


251 


755 


.26 


unk 


C101BB 


na 


205 


2000 


. 36 


unk 


C101CC 


na 


280 


2000 


. 36 


unk 


C101DD 


na 


280 


2000 


. 36 


unk 


FA18L 


575 


450 


2500 


. 33 


unk 


F104GCF 


na 


150 


1566 


.29 


unk 


F14AC 


590 


na 


3409 


. 29 


1735 


F15A 


600 


450 


2604 


. 29 


unk 


F15B 


550 


380 


2604 


. 29 


unk 


F15C 


600 


450 


3005 


. 32 


unk 


F15CFP 


720 


550 


3450 


.45 


unk 


F15D 


550 


400 


3005 


. 32 


unk 


F15E 


670 


490 


3005 


. 32 


unk 


F16A 


550 


440 


2100 


. 31 


unk 


F16B 


500 


400 


2100 


.26 


unk 


F16C 


500 


440 


2100 


.28 


unk 


F16CSC 


500 


440 


2100 


.28 


unk 


F16D 


460 


410 


2100 


.27 


unk 


F16J79 


375 


255 


1575 


.28 


unk 


F20 


410 


385 


1620 


. 31 


unk 


F20A 


410 


385 


1620 


. 31 


unk 


F4CD 


350 


270 


2000 


. 36 


unk 


F4EF 


375 


275 


1610 


. 34 


unk 


F4M0D 


685 


500 


1610 


.40 


unk 


F5A 


290 


187 


1205 


. 28 


unk 


F5B 


290 


187 


1205 


.27 


unk 


F5E 


360 


275 


1345 


.29 


unk 


F5F 


300 


225 


1105 


. 30 


unk 


F86F 


310 


220 


1250 


. 26 


unk 


G91Y 


na 


305 


1890 


. 30 


unk 


HARMK80 


400 


250 


2340 


.28 


unk 


HAWK200 


540 


325 


2200 


.26 


1950 


HAWK50T 


na 


275 


1675 


.28 


unk 


HAWK60A 


440 


275 


2200 


.28 


unk 


HAWK60T 


440 


275 


2200 


.28 


unk 


HUNTER 


490 


290 


1840 


. 19 


unk 


HUNTERT 


525 


300 


1840 


. 19 


unk 


IL28 


na 


538 


2431 


. 34 


1176 


JAGI04 


na 


451 


1902 


. 33 


unk 


JAGI11 


na 


451 


1902 


. 33 


unk 


JASTREB 


na 


170 


820 


.29 


669 


KFIRC2 


470 


415 


2100 


. 26 


unk 


KFIRC7 


540 


420 


2100 


. 26 


unk 


KFIRTC2 


400 


365 


1900 


. 25 


unk 


LAV I 


470 


325 


1050 


. 28 


unk 


LIGHTNG 


432 


260 


1600 


. 30 


unk 


L29 


na 


175 


480 


. 27 


344 


L39ZA 


250 


200 


944 


. 21 


540 


MB326K 


na 


145 


1151 


.21 


unk 


MB326L 


na 


145 


1151 


.21 


unk 


MB339A 


320 


201 


1140 


. 26 


950 


MB339C 


na 


330 


1140 


. 33 


950 


MB339K 


na 


330 


1140 


. 33 


950 


MIG15BIS 


300 


200 


1006 


. 24 


719 



- 155 - 



MIG15UTI 


250 


150 


725 


.25 


513 


MIG17F 


310 


220 


1070 


.24 


444 


MIG19C 


371 


210 


1188 


. 23 


600 


MIG21C 


400 


na 


971 


.25 


unk 


MIG21F 


372 


217 


1147 


. 26 


unk 


MIG21JKL 


400 


200 


971 


.28 


unk 


MIG21R 


na 


280 


1147 


.26 


unk 


MIG21UM 


360 


210 


1147 


.25 


unk 


MIG23B 


470 


385 


1514 


. 36 


unk 


MIG23E 


470 


385 


1514 


. 36 


unk 


MIG23F 


na 


350 


1514 


. 33 


unk 


MIG23G 


470 


385 


1514 


. 36 


unk 


MIG23UM 


420 


330 


1314 


. 32 


unk 


MIG25 


610 


na 


1392 


. 38 


unk 


MIG25R 


na 


487 


1392 


. 38 


unk 


MIG25U 


590 


450 


1392 


. 38 


unk 


MIG27DJ 


na 


460 


1350 


. 34 


unk 


MIG29 


360 


325 


1500 


.26 


unk 


MIG31 


810 


na 


1392 


. 36 


unk 


MIRF1A 


670 


406 


1748 


. 31 


unk 


MIRF1B 


640 


376 


1748 


.31 


unk 


MIRF1C 


670 


446 


1748 


. 31 


unk 


MIRF1E 


700 


450 


2036 


.29 


unk 


MIRIIIC 


416 


na 


2162 


.27 


870 


MIRIIIE 


648 


348 


2162 


.26 


870 


MIRIIIEI 


648 


348 


2162 


.26 


870 


MIR2000C 


378 


280 


2100 


. 28 


800 


MIR2000R 


na 


465 


2100 


.26 


800 


MIR2000T 


358 


260 


2100 


.27 


740 


MIR3NG 


700 


650 


2200 


.26 


unk 


MIR4000 


870 


465 


2100 


.45 


unk 


MIR5DD 


na 


640 


1950 


.28 


unk 


MIR5DR 


na 


700 


2158 


.29 


unk 


MIR5D1 


600 


na 


2158 


. 29 


unk 


MIR5D1E 


600 


na 


2158 


.29 


unk 


MIR5D2 


na 


700 


2158 


.29 


unk 


OV10D 


na 


198 


1243 


. 20 


270 


PRCA5 


na 


348 


1080 


. 31 


unk 


PRCFT6 


370 


200 


1187 


.21 


750 


PRCF6 


370 


249 


1187 


.23 


750 


PRCF7 


400 


200 


971 


.25 


unk 


PRCF7E 


400 


200 


971 


.25 


unk 


RF4C 


na 


306 


2000 


. 34 


unk 


RF5E 


na 


285 


1545 


. 30 


unk 


SF260MW 


na 


260 


926 


. 17 


unk 


SF260TP 


na 


260 


925 


. 20 


512 


SUPETEN 


na 


351 


1782 


.28 


unk 


SU20 


na 


340 


1220 


.27 


unk 


SU22 


na 


378 


1480 


.28 


unk 


SU25 


na 


300 


1500 


. 37 


unk 


SU27 


810 


350 


1500 


.28 


900 


SU7BMKL 


na 


261 


783 


. 21 


436 


SU7U 


na 


187 


780 


.21 


436 


TA4EH 


na 


250 


2500 


. 35 


unk 


TA4KU 


na 


255 


1500 


. 33 


1500 


TORADV 


750 


na 


2100 


. 33 


unk 


TORIDS 


na 


751 


2100 


. 31 


unk 


TU16AG 


na 


1565 


3000 


.41 


2605 


TU22BD 


na 


1670 


3200 


. 50 


unk 



- 156 



ACFT 

ALPHAMS1 

ALPHAMS2 

AMX 

A10A 

A37B 

A4H 

A4KU 

A4N 

A7E 

A7P 

BAC167 

CM170 

CM170I 

C101BB 

C101CC 

C101DD 

FA18L 

F104GCF 

F14AC 

F15A 

F15B 

F15C 

F15CFP 

F15D 

F15E 

F16A 

F16B 

F16C 

F16CSC 

F16D 

F16J79 

F20 

F20A 

F4CD 

F4EF 

F4MOD 

F5A 

F5B 

F5E 

F5F 

F86F 

G91Y 

HARMK80 

HAWK200 

HAWK50T 

HAWK60A 

HAWK60T 

HUNTER 

HUNTERT 

IL28 

JAGI04 

JAGI11 

JASTREB 

KFIRC2 

KFIRC7 

KFIRTC2 

LAV I 

LIGHTNG 

L29 

L39ZA 

MB326K 

MB326L 

MB339A 

MB339C 

MB339K 

MIG15BIS 



MAXORD 


STNS 


NGUN 


CAL 


ROUNDS 


5510 


5 











5510 


5 


1 


30 


125 


8377 


5 


1 


20 


350 


14341 


10 


1 


30 


1174 


5680 


6 


1 


8 


200 


8600 


5 


2 


20 


400 


8600 


5 


2 


20 


400 


9470 


7 


2 


30 


300 


15000 


6 


1 


20 


1032 


14250 


6 


1 


20 


1032 


3000 


4 


2 


8 


200 


330 


2 


2 


8 


360 


330 


2 


2 


8 


360 


4960 


6 


1 


30 


200 


4960 


6 


2 


13 


200 


4960 


6 


2 


13 


200 


17000 


8 


1 


20 


570 


7500 


7 


1 


20 


725 


14500 


8 


1 


20 


675 


15500 


5 


1 


20 


940 


15500 


5 


1 


20 


940 


15500 


5 


1 


20 


940 


16000 


7 


1 


20 


940 


15500 


5 


1 


20 


940 


23500 


9 


1 


20 


940 


15200 


7 


1 


20 


515 


15200 


7 


1 


20 


515 


15200 


7 


1 


20 


515 


15200 


7 


1 


20 


515 


15200 


7 


1 


20 


515 


11950 


7 


1 


20 


515 


8300 


7 


2 


20 


900 


8300 


7 


2 


20 


900 


16000 


6 











19080 


6 


1 


20 


639 


23080 


9 


1 


20 


639 


6200 


5 


2 


20 


280 


6200 


5 


2 


20 


280 


7000 


5 


2 


20 


280 


7000 


5 


1 


20 


280 


2000 


2 


6 


13 


200 


4000 


4 


2 


30 


200 


8000 


5 


2 


30 


250 


6800 


5 


2 


30 


300 


1540 


3 


1 


30 


120 


6800 


5 


1 


30 


120 


1540 


5 


1 


30 


120 


7100 


4 


2 


30 


200 


7100 


4 


2 


30 


200 


6614 


6 


4 


23 


650 


10500 


6 


2 


30 


300 


10500 


6 


2 


30 


300 


2420 


6 


3 


. 5 


405 


8500 


7 


2 


30 


280 


12250 


7 


2 


30 


280 


8500 


7 


2 


30 


280 


6000 


10 


2 


30 


280 


6000 


6 


2 


30 


240 


440 


2 


2 


8 


200 


2425 


4 


1 


23 


150 


4000 


6 


2 


30 


200 


4000 


6 





30 


200 


4000 


6 











4270 


6 


2 


30 


280 


4270 


6 


2 


30 


280 


2000 


2 


2 


23 


160 



157 



MIG15UTI 








2 


23 


200 


MIG17F 


1650 


2 


3 


23 


200 


MIG19C 


2900 


2 


3 


30 


200 


MIG21C 


2000 


2 


1 


23 


200 


MIG21F 


4400 


3 


1 


23 


200 


MIG21JKL 


4400 


3 


1 


23 


200 


MIG21R 


2000 


2 


1 


23 


200 


MIG21UM 


4400 


3 


1 


23 


200 


MIG23B 


4400 


4 


1 


23 


200 


MIG23E 


4400 


4 


1 


23 


200 


MIG23F 


4400 


4 


1 


23 


200 


MIG23G 


4400 


4 


1 


23 


200 


MIG23UM 


4400 


4 


1 


23 


200 


MIG25 


8000 


4 











MIG25R 


8000 


4 











MIG25U 


8000 


4 











MIG27DJ 


6615 


5 


1 


23 


500 


MIG29 


8800 


6 


1 


23 


200 


MIG31 


12000 


6 


1 


30 


360 


MIRF1A 


8820 


5 


2 


30 


270 


MIRF1B 


8820 


5 


2 


30 


270 


MIRF1C 


8820 


5 


2 


30 


270 


MIRF1E 


8820 


5 


2 


30 


270 


MIRIIIC 


3000 


3 


2 


30 


250 


MIRIIIE 


8818 


5 


2 


30 


250 


MIRIIIEI 


8818 


7 


2 


30 


250 


MIR2000C 


13890 


7 


2 


30 


250 


MIR2000R 


1250 


4 


2 


30 


200 


MIR2000T 


13890 


7 


2 


30 


250 


MIR3NG 


9260 


7 


2 


30 


250 


MIR4000 


17635 


9 


2 


30 


200 


MIR5DD 


8000 


5 


2 


30 


250 


MIR5DR 


8818 


2 


2 


30 


250 


MIR5D1 


400 


2 


2 


30 


250 


MIR5D1E 


400 


2 


2 


30 


250 


MIR5D2 


9260 


5 


2 


30 


250 


OV10D 


3600 


5 


2 


8 


1000 


PRCA5 


4410 


5 


2 


23 


500 


PRCFT6 








1 


30 


200 


PRCF6 








2 


23 


200 


PRCF7 


2000 


2 


1 


23 


200 


PRCF7E 


2000 


2 


1 


23 


200 


RF4C 


400 


2 











RF5E 


400 


2 


1 


20 


280 


SF260MW 


661 


4 


2 


8 





SF260TP 


661 


4 











SUPETEN 


4630 


6 


2 


30 


250 


SU20 


8820 


8 


2 


30 


140 


SU22 


11023 


8 


2 


30 


140 


SU25 


8820 


10 


1 


30 


200 


SU27 


13225 


6 


1 


23 


200 


SU7BMKL 


5500 


4 


2 


30 


140 


SU7U 


5500 


4 


2 


30 


140 


TA4EH 


8200 


5 


2 


20 


400 


TA4KU 


8200 


5 


2 


20 


400 


TORADV 


18000 


6 


1 


27 


200 


TORIDS 


19840 


9 


2 


27 


200 


TU16AG 


19800 


8 


7 


23 


200 


TU22BD 


26450 


10 


1 


23 


200 



158- 



B.2 Target Acquisition Systems 

NAME CODE PWR CONE UPRNG DWNRNG DATAPTS 



AGAVE 


RAMU 


100 


140 


10 





2 


AIDAII 


RAGA 


80 


18 





10 


2 


AIRPASSI 


RAAI 


900 


90 


80 





I 


ANTILOPE 


RAMU 


500 


120 


50 


40 


APG63 


RAMU 


1300 


120 


100 


37 


4 


APG64 


RAMU 


1300 


120 


120 


47 


4 


APG65 


RAMU 


500 


120 


45 


34 


4 


APG66 


RAMU 


400 


120 


38 


29 


3 


APG67 


RAMU 


330 


160 


47 


38 


4 


APG68 


RAMU 


400 


120 


51 


47 


4 


APG69 


RAMU 


80 


90 


20 


14 


4 


APG70 


RAMU 


1300 


120 


120 


50 


4 


APN153V 


RAGA 


80 


90 





10 


2 


APQ109 


RAMU 


150 


90 


20 





3 


APQ120 


RAMU 


200 


90 


25 





3 


APQ159 


RAAI 


80 


90 


10 





3 


AWG9 


RAAI 


1300 


120 


110 


80 


4 


BLUEFOX 


RAMU 


200 


120 


30 


15 


4 


CYRI 


RAAI 


100 


120 


14 





3 


CYRII 


RAMU 


200 


120 


30 





3 


CYRIV 


RAAI 


200 


120 


30 





4 


CYRIVM3 


RAMU 


200 


120 


30 


15 


4 


CYRIV2 


RAMU 


200 


120 


30 


15 


4 


ELM2001B 


RAMU 


200 


90 


30 





2 


ELM2021B 


RAMU 


200 


90 


35 


25 


4 


ELTAFIAR 


RAGA 


200 


90 





30 


2 


FLANRAD 


RAMU 


1200 


120 


130 


40 


4 


FOXFIRE 


RAAI 


600 


120 


50 





4 


FOXHUNT 


RAMU 


1200 


120 


97 


70 


4 


FULRAD 


RAMU 


400 


90 


40 


30 


3 


HIFIX 


RAMU 


80 


40 


4 





2 


HILARKI 


RAMU 


200 


90 


25 





4 


HILARKII 


RAMU 


300 


90 


35 


15 


4 


HILARKX 


RAAI 


400 


120 


40 


20 


4 


HOUNDRAD 


RAAI 


1200 


120 


100 


50 


4 


IRSTSB 


IRAI 


80 


40 


15 


10 


2 


IRSTSG 


IRAI 


100 


60 


20 


15 


2 


JAYBIRD 


RAAI 


150 


90 


18 





3 


LASDES 


LAGA 


80 


30 





2 


2 


LASRNG 


LAGA 


80 


20 





2 


2 


RDAL2 


RAGA 


200 


90 





20 





RDI 


RAAI 


600 


120 


54 


20 


4 


RDM 


RAMU 


600 


120 


60 


20 


4 


SCANFIX 


RAAI 


80 


60 


4 





2 


SCANODD 


RAAI 


80 


60 


6 





2 


SHRTHRN 


RAGA 


200 


90 





30 


2 


SKYRNGR 


RAAI 


80 


90 


9 





2 


SPNSCNA 


RAAI 


100 


60 


11 





2 


SPNSCNB 


RAAI 


100 


60 


11 





2 


TI-ATA 


RAMU 


300 


120 


30 


20 


4 


TI-ATG 


RAMU 


300 


120 


80 


20 


4 


VISUAL 


VIMU 


40 


30 


10 


3 


1 



- 159- 



NAME TWS ILLUM MAP DBS ECCM 



AGAVE 





1 


1 


1 


. 7 


AIDAII 














. 7 


AIRPASSI 














. 9 


ANTILOPE 


1 





1 


1 


1. 1 


APG63 


1 


1 





1 


1. 


APG64 


1 


1 





1 


1. 1 


APG65 


1 


1 


1 


1 


1.0 


APG66 








1 


1 


1.0 


APG67 


1 





1 


1 


1. 1 


APG68 


1 


1 


1 


1 


1. 1 


APG69 


1 


1 





1 


1.0 


APG70 


1 


1 


1 


1 


1. 1 


APN153V 














.8 


APQ109 





1 








. 7 


APQ120 





1 


1 





.8 


APQ159 














.8 


AWG9 


1 


1 


1 


1 


1.0 


BLUEFOX 








1 





. 9 


CYRI 





1 








. 7 


CYRII 





1 


1 





.8 


CYRIV 














. 9 


CYRIVM3 


1 


1 


1 


1 


1. 1 


CYRIV2 





1 





1 


1.0 


ELM2001B 














. 9 


ELM2021B 


1 





1 


1 


1. 1 


ELTAFIAR 














. 9 


FLANRAD 


1 


1 





1 


1.0 


FOXFIRE 





1 


1 





.8 


FOXHUNT 


1 


1 








1. 


FULRAD 





1 


1 


1 


1. 


HIFIX 














. 7 


HILARKI 





1 








. 8 


HILARKII 





1 








1.0 


HILARKX 


1 


1 


1 


1 


1.0 


HOUNDRAD 


1 


1 


1 


1 


1. 1 


IRSTSB 














.8 


IRSTSG 














.8 


JAYBIRD 





1 








.8 


LASDES 





1 








1.0 


LASRNG 














1.0 


RDA12 














.8 


RDI 


1 


1 


1 


1 


1. 1 


RDM 


1 


1 


1 


1 


1. 1 


SCANFIX 





1 








. 7 


SCANODD 














. 7 


SHRTHRN 








1 





. 8 


SKYRNGR 














. 8 


SPNSCNA 





1 








. 8 


SPNSCNB 





1 








. 8 


TI-ATA 


1 


1 








1. 1 


TI-ATG 








1 


1 


1. 1 


VISUAL 














1.0 



- 160 - 



B.3 Air-to-Air Missiles 



MSL 



CODE DIAM LENGTH MSLWGHT WHWGHT 



AA2B 


AAMI 


4. 7 


110.0 


190. 


13. 


2 


AA2C 


AAMR 


4. 7 


114.0 


190. 


13. 


2 


AA2D 


AAMI 


4. 7 


110. 


190. 


13. 


2 


AA6A 


AAMR 


15. 7 


232. 


1565 


88. 





AA6B 


AAMI 


15. 7 


248. 


1565 


88. 





AA7A 


AAMR 


8.8 


181.0 


705. 


88. 





AA7B 


AAMI 


8.8 


177. 


660. 


88. 





AA8B 


AAMI 


4. 7 


84. 6 


121. 


17. 





AA9A 


AAMR 


8. 8 


170.0 


650. 


100. 





AIM120A 


AAMR 


7.0 


145. 7 


326.0 


50. 





AA10A 


AAMR 


7.0 


145. 7 


326.0 


50. 





AIM9D 


AAMI 


4.0 


113.0 


195.0 


22. 


4 


AIM9E 


AAMI 


4. 


118. 1 


164.0 


10. 





AIM9G 


AAMI 


4.0 


113. 


191.0 


22. 


4 


AIM9H 


AAMI 


4.0 


113.0 


186.0 


22. 


4 


AIM9J 


AAMI 


4.0 


120. 9 


172. 


10. 





AIM9L 


AAMI 


4.0 


112.2 


188.0 


25. 





AIM9M 


AAMI 


4.0 


112.2 


190. 


25. 





AIM9PN 


AAMI 


4.0 


120. 9 


172.0 


10. 





SKYFLASH 


AAMR 


9.0 


145. 


425.0 


66. 





AIM7C 


AAMR 


8.0 


144.0 


380. 


66. 





AIM7D 


AAMR 


8.0 


144.0 


440.0 


66. 





AIM7E 


AAMR 


8. 


144.0 


452.0 


66. 





AIM7F 


AAMR 


8. 


144.0 


503. 


88. 





AIM7M 


AAMR 


8. 


145.0 


503.0 


88. 





KUKRI 


AAMI 


5. 


115. 9 


161.5 


10. 





ASPIDE 


AAMR 


8. 


145. 5 


485.0 


72. 


8 


FIRESTRK 


AAMI 


8. 8 


125. 5 


300.0 


50. 





R550 


AAMI 


6.2 


109.0 


198. 


27. 


6 


STINGER 


AAMI 


2.8 


60.0 


22. 3 


6. 


6 


AIM54 


AAMR 


15.0 


157. 8 


985.0 


132. 





PIRANHA 


AAMI 


6.0 


105. 


190.0 


26. 


5 


PYTHON3 


AAMI 


6. 3 


97. 


200.0 


24. 





R530R 


AAMR 


10.4 


129. 3 


423. 3 


60. 





R530I 


AAMI 


10.4 


125. 9 


426. 6 


60. 





SUP530F 


AAMR 


10.4 


139.4 


551. 


66. 





RBS70 


AAMI 


4. 2 


52. 


33. 


2. 


2 


REDTOP 


AAMI 


8.8 


130. 6 


330.0 


68. 


3 


SHAFRIR 


AAMI 


6. 3 


97.0 


205. 


24. 


3 


R550MK2 


AAMI 


6.2 


109.0 


198. 


27. 


6 


SUP530D 


AAMR 


10.4 


139.4 


500. 


66. 






- 161 - 



MSL 



GUIDTYP GUIDSC MODE MSPD LIMG ECCM 



AA2B 


IR 


• 


9 


VR 


2. 


5 


25 


1. 1 


AA2C 


SARH 


. 


8 


VR 


2. 


5 


30 


. 9 


AA2D 


IR 


. 


9 


VR 


2. 


5 


30 


. 9 


AA6A 


SARH 


1. 





BVR 


2. 


2 


16 


. 9 


AA6B 


IR 


1. 





VR 


2. 


2 


16 


. 8 


AA7A 


SARH 


1. 





BVR 


3. 





15 


. 9 


AA7B 


IR 


1. 





VR 


3. 





15 


. 9 


AA8B 


IR 


1. 





VR 


3. 





30 


. 8 


AA9A 


SARH 


1. 





BVR 


4. 





15 


. 7 


AIM120A 


ARH 


1. 


2 


BVR 


4. 





30 


. 7 


AA10A 


SARH 


• 


8 


BVR 


4, 





30 


. 7 


AIM9D 


IR 


• 


9 


VR 


2. 


5 


25 


1.0 


AIM9E 


IR 




9 


VR 


2. 


5 


25 


. 9 


AIM9G 


IR 


1. 





VR 


2. 


5 


25 


. 9 


AIM9H 


IR 


1. 





VR 


2. 


5 


25 


. 9 


AIM9J 


IR 


1. 





VR 


2. 


5 


30 


. 8 


AIM9L 


IR 


1. 





VR 


2. 


5 


30 


. 8 


AIM9M 


IR 


1. 





VR 


2. 


5 


30 


. 7 


AIM9PN 


IR 


1. 





VR 


2. 


5 


30 


. 9 


SKYFLASH 


SARH 


1. 





BVR 


4. 





16 


. 8 


AIM7C 


SARH 




8 


VR 


3. 


5 


16 


1.0 


AIM7D 


SARH 


• 


8 


VR 


3. 


5 


16 


1. 


AIM7E 


SARH 


• 


8 


VR 


3. 


7 


20 


. 9 


AIM7F 


SARH 


1. 





BVR 


4. 





20 


. 8 


AIM7M 


SARH 


1. 





BVR 


4. 





20 


. 7 


KUKRI 


IR 


1. 





VR 


1. 


8 


35 


. 9 


ASPIDE 


SARH 


1. 





BVR 


4. 





15 


.8 


FIRESTRK 


IR 


. 


9 


VR 


3. 





20 


1. 


R550 


IR 


1. 





VR 


3. 





25 


1.0 


STINGER 


IR 


1. 





VR 


1. 


5 


20 


. 9 


AIM54 


ARH 


1. 


2 


BVR 


5. 





20 


. 8 


PIRANHA 


IR 


1. 





VR 


2. 


2 


25 


. 9 


PYTHON3 


IR 


1. 





VR 


2. 


5 


30 


.8 


R530R 


SARH 


, 


8 


VR 


2. 


7 


25 


. 9 


R530I 


IR 


1. 





VR 


2. 


7 


25 


1.0 


SUP530F 


SARH 


• 


8 


VR 


4. 


6 


25 


.8 


RBS70 


LASR 


• 


7 


VR 


1. 


5 


25 


. 9 


REDTOP 


IR 


1. 





VR 


3. 


2 


20 


1.0 


SHAFRIR 


IR 


1. 





VR 


2. 


5 


25 


. 8 


R550MK2 


IR 


1. 





VR 


3. 





30 


. 7 


SUP530D 


SARH 


1. 





BVR 


4. 


6 


25 


. 7 



- 162 



MSL 

AA2B 

AA2C 

AA2D 

AA6A 

AA6B 

AA7A 

AA7B 

AA8B 

AA9A 

AIM120A 

AA10A 

AIM9D 

AIM9E 

AIM9G 

AIM9H 

AIM9J 

AIM9L 

AIM9M 

AIM9PN 

SKYFLASH 

AIM7C 

AIM7D 

AIM7E 

AIM7F 

AIM7M 

KUKRI 

ASPIDE 

FIRESTRK 

R550 

STINGER 

AIM54 

PIRANHA 

PYTHON3 

R530R 

R530I 

SUP530F 

RBS70 

REDTOP 

SHAFRIR 

R550MK2 

SUP530D 



MAXHRNG MINHRNG EFFHRNG MAXTRNG MINTRNG EFFTRNG 



.0 
.0 
. 
30.0 
.0 
25.0 
20.0 
. 
35. 
27. 
25.0 
.0 
.0 
.0 
.0 
. -0 
13.4 
13.4 
. 
26. 3 
21.8 
21.8 
24.4 
53. 9 
53. 9 
.0 
26.2 
.0 
.0 
2. 6 
108 
.0 
8. 1 
7.0 
7.0 
18. 9 
2. 7 
6.5 
. 
7. 6 
37.0 



2. 

1. 









2 





1 

.0 

2.0 

2.0 

2.0 

.0 

.0 

.0 

.0 

.0 

.8 

1.0 

. 

3. 

3. 

3. 

3.0 

2.0 

2. 
. 

2.0 
.0 
.0 

1.0 

2.5 
. 

1. 1 

3. 
3. 
3.0 

1. 1 

2. 5 
.0 
. 7 

1. 



.00 

. 00 

. 00 

27. 80 

. 00 

23. 00 

18. 90 

00 



33, 
25, 
23, 



00 
00 
00 
00 
00 
.00 
.00 
. 00 
12. 60 
12.40 
. 00 
23. 30 
18. 80 
18. 80 
21.40 
51. 90 
51. 90 
.00 
24.20 
. 00 
. 00 
1. 60 
105. 5 
. 00 
7. 00 
4. 00 
4.00 
15. 90 
1. 60 
4.00 
. 00 
6. 90 
36. 00 



3.5 

8. 

8. 

10. 

15. 5 

10.0 

8. 1 
3. 

12. 

10. 8 

8.8 

9. 6 
2. 3 
9. 6 



9 
7 
9 
9 
9 

7.0 

11.0 

11.0 

12.0 

18.0 

18. 

2.2 

12.0 

4. 3 

5.4 

2.4 

36. 

3. 2 

3.2 

2.8 

2. 8 

7. 6 

1. 1 

2. 6 
2. 7 
5.4 

14.8 



1. 

1. 



5 

5 
5 
1 
1 
5 
5 

5 
5 
5 
6 
6 
4 
4 
4 
2 
4 
2 
1 
1 
1 
1 
. 5 
.5 
.2 
. 5 
. 6 
.2 
.4 
1.0 
. 5 
. 3 
. 8 
. 8 
.8 
. 3 
. 6 
. 5 
. 2 
. 3 



1. 
1. 
1. 
1. 



3.00 
7. 50 

7. 50 

8. 90 
14.40 

9. 50 



7, 

2, 
11, 
10. 

8. 

9. 

1. 

9 



60 
93 
50 
30 
30 
00 
70 
20 



9. 20 
7.40 
9.40 
9. 20 
9.40 

5. 90 
9. 90 
9. 90 

10. 90 
17. 50 
17. 50 

2. 04 

11. 50 

3. 65 
5.23 
2. 00 

35. 00 
2. 70 
2. 93 
1. 96 
1. 96 

6. 76 
.85 

1. 95 

2. 20 
5.23 

14.53 



163 



B.4 Aerial Guns 



GUN 



CODE CAL MRNG DISP MVEL RATE 



ADENMK4 


ACCI 


30. 





1. 


000 


5. 





2600 


1400 


ADENMK5 


ACCI 


30. 





1. 


100 


4. 


5 


3100 


1700 


CB. 50 


ACCI 


7. 


6 




593 


5. 





2750 


550 


DEFA552A 


ACCI 


30. 





■ 


500 


2. 


5 


2400 


1300 


DEFA553 


ACCI 


30. 





, 


750 


2. 


2 


2400 


1300 


DEFA554 


ACCI 


30. 





1. 


000 


2. 





2700 


1800 


FN7. 62 


ACCI 


7. 


6 


, 


593 


5. 





2750 


550 


GAU12U 


ACCI 


25. 





1. 


100 


6. 





3600 


4200 


GAU13A 


ACCE 


30. 





1. 


200 


2. 





3400 


2400 


GAU2BA 


ACCE 


7. 


6 


• 


806 


6. 


5 


2700 


4000 


GAU8A 


ACCI 


30. 





1. 


187 


5. 





3500 


4200 


GPU5A 


ACCE 


30. 





1. 


000 


2. 





3000 


2400 


GSH23 


ACCI 


23. 





■ 


243 


4. 


5 


2350 


3000 


HGS55 


ACCE 


7. 


6 


, 


560 


5. 





2800 


570 


HIS404 


ACCI 


20. 





■ 


863 


2. 


5 


2800 


640 


KCA30 


ACCE 


30. 





1. 


079 


2. 


5 


3380 


1350 


MAU27 


ACCI 


27. 





1. 


000 


2. 





3380 


2400 


MKIIMOD5 


ACCE 


20. 





■ 


513 


2. 





3380 


4200 


M16 


ACCE 


7. 


6 


, 


539 


5. 





2700 


2600 


M197 


ACCE 


20. 





• 


500 


2. 


2 


3400 


3000 


M230 


ACCE 


30. 





1. 


100 


5. 





2600 


625 


M28 


ACCE 


7. 


7 


, 


806 


6. 


5 


2700 


4000 


M39 


ACCI 


20. 





, 


500 


2. 


2 


2800 


3000 


M5 


ACCE 


40. 





, 


806 


5. 





790 


230 


M61A1 


ACCI 


20. 





• 


539 


2. 


2 


3380 


4000 


M621 


ACCE 


20. 





• 


809 


2. 





3380 


740 


NR23 


ACCI 


23. 





, 


197 


4. 





1200 


850 


NR23HS 


ACCI 


23. 





, 


197 


4. 





1250 


900 


NR30 


ACCI 


30. 





• 


248 


3. 


5 


2550 


850 


NR30GAT 


ACCI 


30. 





• 


329 


4. 





2700 


5150 


N37 


ACCI 


37. 





• 


197 


4. 





1200 


400 


N37D 


ACCI 


37. 





• 


197 


4. 





2250 


400 


UBK 


ACCE 


12. 


7 


• 


809 


4. 


5 


2900 


700 


US12. 7 


ACCI 


12. 


7 


• 


800 


5. 





2900 


700 


XM188E30 


ACCE 


30. 





1. 


150 


5. 





2600 


2000 


XM27E1 


ACCE 


7. 


6 


• 


592 


5. 





2850 


4000 


XM8 


ACCE 


40. 





1. 


187 


5. 





790 


400 



- 164- 



B.5 Air Weapon System Configuration 

ACFT PRODCC CREW ARC NAVCAT MMHFH 

2 TAC 18 

2 DOP 20 

1 INS 20 

1 1 INS 18 

2 TAC 16 
1 1 INS 30 
1 DOP 29 
1 1 INS 30 
1 1 INS 53 

1 DOP 45 

2 TAC 20 
2 TAC 18 
2 TAC 20 
2 TAC 20 
2 TAC 20 
2 TAC 18 
1 1 INS 24 

1 1 INS 45 

2 1 INS 60 

1 1 INS 41 

2 1 INS 41 
1 1 INS 34 
1 1 INS 34 

1 1 INS 34 

2 1 INS 34 

1 1 INS 30 

2 1 INS 30 
1 1 INS 25 

1 1 INS 23 

2 1 INS 25 
1 1 INS 25 
1 1 INS 15 

1 1 INS 17 

2 1 INS 38 
2 1 INS 38 
2 1 INS 38 

1 TAC 16 

2 TAC 16 

1 INS 20 

2 INS 20 
1 DR 40 
1 TAC 20 
1 1 DOP 44 

1 INS 24 

2 TAC 20 
1 TAC 24 
1 TAC 20 

1 DR 44 

2 DR 40 

3 TAC 60 
1 1 INS 38 
1 1 INS 38 
1 TAC 18 
1 INS 18 

1 INS 18 

2 INS 15 
1 INS 26 

1 DR 40 

2 DR 19 
2 TAC 19 

1 DR 20 

2 DR 18 
2 TAC 18 
1 INS 22 

- 165- 



ALPHAMS1 


FR 


ALPHAMS2 


FR 


AMX 


IT 


A10A 


US 


A37B 


US 


A4H 


US 


A4KU 


US 


A4N 


US 


A7E 


US 


A7P 


US 


BAC167 


UK 


CM170 


FR 


CM170I 


IS 


C101BB 


SP 


C101CC 


SP 


C101DD 


SP 


FA18L 


US 


F104GCF 


US 


F14AC 


US 


F15A 


US 


F15B 


US 


F15C 


US 


F15CFP 


us 


F15D 


us 


F15E 


us 


F16A 


us 


F16B 


us 


F16C 


us 


F16CSC 


us 


F16D 


us 


F16J79 


us 


F20 


us 


F20A 


us 


F4CD 


us 


F4EF 


us 


F4MOD 


us 


F5A 


us 


F5B 


us 


F5E 


us 


F5F 


us 


F86F 


us 


G91Y 


IT 


HARMK80 


UK 


HAWK200 


UK 


HAWK50T 


UK 


HAWK60A 


UK 


HAWK60T 


UK 


HUNTER 


UK 


HUNTERT 


UK 


IL28 


UR 


JAGI04 


UK 


JAGI11 


UK 


JASTREB 


YU 


KFIRC2 


IS 


KFIRC7 


IS 


KFIRTC2 


IS 


LAV I 


IS 


LIGHTNG 


UK 


L29 


CZ 


L39ZA 


CZ 


MB326K 


IT 


MB326L 


IT 


MB339A 


IT 


MB339C 


IT 



MB339K 


IT 


1 





TAC 


20 


MIG15BIS 


UR 


1 





DR 


18 


MIG15UTI 


UR 


2 





DR 


16 


MIG17F 


UR 


1 





DR 


17 


MIG19C 


UR 


1 





DR 


17 


MIG21C 


UR 


1 





DR 


18 


MIG21F 


UR 


1 





DR 


18 


MIG21JKL 


UR 


1 





DOP 


18 


MIG21R 


UR 


1 





DOP 


22 


MIG21UM 


UR 


2 





DR 


18 


MIG23B 


UR 


1 





DOP 


38 


MIG23E 


UR 


1 





DOP 


36 


MIG23F 


UR 


1 





DOP 


40 


MIG23G 


UR 


1 





DOP 


38 


MIG23UM 


UR 


2 





DOP 


36 


MIG25 


UR 


1 





DOP 


32 


MIG25R 


UR 


1 





DOP 


32 


MIG25U 


UR 


1 





DOP 


32 


MIG27DJ 


UR 


1 





DOP 


42 


MIG29 


UR 


1 





INS 


25 


MIG31 


UR 


2 





INS 


50 


MIRF1A 


FR 


1 





INS 


38 


MIRF1B 


FR 


2 





INS 


38 


MIRF1C 


FR 


1 





INS 


34 


MIRF1E 


FR 


1 





INS 


34 


MIRIIIC 


FR 


1 





DOP 


38 


MIRIIIE 


FR 


1 





DOP 


38 


MIRIIIEI 


FR 


1 





INS 


38 


MIR2000C 


FR 


1 





INS 


28 


MIR2000R 


FR 


1 





INS 


30 


MIR2000T 


FR 


2 





INS 


28 


MIR3NG 


FR 


1 





DOP 


33 


MIR4000 


FR 


1 


1 


INS 


30 


MIR5DD 


FR 


2 





DOP 


36 


MIR5DR 


FR 


1 





DOP 


40 


MIR5D1 


FR 


1 





DOP 


38 


MIR5D1E 


FR 


1 





DOP 


38 


MIR5D2 


FR 


1 





DOP 


40 


OV10D 


US 


2 





TAC 


16 


PRCA5 


CH 


1 





TAC 


22 


PRCFT6 


CH 


2 





DR 


16 


PRCF6 


CH 


1 





DR 


16 


PRCF7 


CH 


1 





TAC 


18 


PRCF7E 


EG 


1 





TAC 


18 


RF4C 


US 


2 


1 


INS 


42 


RF5E 


US 


1 





INS 


22 


SF260MW 


IT 


3 





DR 


16 


SF260TP 


IT 


3 





DR 


16 


SUPETEN 


FR 


1 


1 


INS 


33 


SU20 


UR 


1 





DOP 


26 


SU22 


UR 


1 





DOP 


26 


SU25 


UR 


1 





TAC 


18 


SU27 


UR 


1 





INS 


41 


SU7BMKL 


UR 


1 





DR 


18 


SU7U 


UR 


2 





DR 


16 


TA4EH 


US 


2 


1 


DOP 


29 


TA4KU 


US 


2 





DOP 


29 


TORADV 


UK 


2 


1 


INS 


30 


TORIDS 


UK 


2 


1 


INS 


34 


TU16AG 


UR 


6 


1 


DR 


70 


TU22BD 


UR 


3 





DR 


70 



166 



ACFT 



TARAD 



TAOTH 



RWR PECM AECM 



ALPHAMS1 

















ALPHAMS2 





LASRNG 








1 


AMX 


ELTAFIAR 


LASRNG 


1 


1 





A10A 





LASRNG 


1 


1 


1 


A37B 











1 





A4H 


APN153V 





1 





1 


A4KU 


APN153V 





1 








A4N 


APN153V 





1 





1 


A7E 


APQ126 
APQ126 


LASRNG 


1 


1 


1 


A7P 





1 


1 





BAC167 

















CM170 

















CM170I 

















C101BB 

















C101CC 

















C101DD 

















FA18L 


APG65 


LASRNG 


1 


1 


1 


F104GCF 

















F14AC 


AWG9 





1 


1 


1 


F15A 


APG63 





1 


1 


1 


F15B 


APG63 





1 


1 


1 


F15C 


APG64 





1 


1 


1 


F15CFP 


APG64 





1 


1 


1 


F15D 


APG64 





1 


1 


1 


F15E 


APG70 


LASDES 


1 


1 


1 


F16A 


APG66 


LASRNG 


1 


1 


1 


F16B 


APG66 


LASRNG 


1 


1 


1 


F16C 


APG68 


LASRNG 


1 


1 


1 


F16CSC 


APG66 





1 


1 





F16D 


APG68 


LASRNG 


1 


1 


1 


F16J79 


APG66 





1 


1 





F20 


APG67 





1 


1 





F20A 


APG67 





1 


1 


1 


F4CD 


APQ109 





1 


1 


1 


F4EF 


APQ120 


LASDES 


1 


1 


1 


F4M0D 


APG65 


LASDES 


1 


1 


1 


F5A 











1 





F5B 











1 





F5E 


APQ159 





1 


1 


1 


F5F 


APQ159 


LASDES 


1 


1 


1 


F86F 

















G91Y 


RDA12 














HARMK80 





LASDES 


1 


1 


1 


HAWK200 


BLUEFOX 


LASRNG 


1 





1 


HAWK50T 

















HAWK60A 





LASRNG 











HAWK60T 

















HUNTER 

















HUNTERT 

















IL28 








1 








JAGI04 





LASRNG 


1 


1 





JAGI11 





LASRNG 


1 


1 





JASTREB 

















KFIRC2 


ELM2001B 





1 


1 


1 


KFIRC7 


ELM2021B 





1 


1 


1 


KFIRTC2 








1 


1 


1 


LAV I 


ELM2021B 





1 


1 


1 


LIGHTNG 


AIRPASSI 














L29 

















L39ZA 

















MB326K 

















MB326L 

















MB339A 








1 


1 





MB339C 





LASRNG 


1 


1 





MB339K 








1 


1 





MIG15BIS 


















167. 



MIG15UTI 

















MIG17F 


SCANODD 














MIG19C 


SCANFIX 





1 








MIG21C 


SPNSCNA 





1 








MIG21F 


SPNSCNB 





1 








MIG21JKL 


JAYBIRD 





1 








MIG21R 


SPNSCNB 





1 








MIG21UM 


SPNSCNB 





1 








MIG23B 


HILARKI 


IRSTSB 


1 





1 


MIG23E 


JAYBIRD 





1 








MIG23F 





LASRNG 


1 








MIG23G 


HILARKI I 


IRSTSG 


1 





1 


MIG23UM 


JAYBIRD 





1 








MIG25 


FOXFIRE 





1 





1 


MIG25R 


FOXFIRE 





1 





1 


MIG25U 


FOXFIRE 





1 





1 


MIG27DJ 





LASRNG 


1 


1 


1 


MIG29 


FULRAD 


LASRNG 


1 


1 


1 


MIG31 


HOUNDRAD 





1 


1 


1 


MIRF1A 


AIDAII 


LASRNG 


1 





1 


MIRF1B 


AIDAII 





1 





1 


MIRF1C 


CYRIV2 


LASRNG 


1 





1 


MIRF1E 


CYRIVM3 


LASRNG 


1 





1 


MIRIIIC 


CYRII 





1 








MIRIIIE 


CYRIV 





1 








MIRIIIEI 


CYRIV 





1 


1 


1 


MIR2000C 


RDM 


LASDES 


1 


1 


1 


MIR2000R 


RDM 





1 


1 


1 


MIR2000T 


RDM 


LASDES 


1 


1 


1 


MIR3NG 


CYRIVM3 





1 





1 


MIR4000 


RDI 


LASDES 


1 


1 


1 


MIR5DD 


AIDAII 





1 








MIR5DR 


AIDAII 





1 








MIR5D1 


AGAVE 





1 








MIR5D1E 


CYRIVM3 





1 








MIR5D2 


AGAVE 





1 








OV10D 





LASDES 


1 


1 





PRCA5 








1 








PRCFT6 


SCANFIX 














PRCF6 


SCANFIX 














PRCF7 


SPNSCNB 





1 








PRCF7E 


SPNSCNB 





1 








RF4C 


APQ109 
APQ159 





1 


1 





RF5E 





1 


1 


1 


SF260MW 

















SF260TP 

















SUPETEN 


AGAVE 





1 





1 


SU20 


HIFIX 





1 


1 


1 


SU22 


HIFIX 


LASDES 


1 


1 


1 


SU25 





LASRNG 


1 


1 





SU27 


FLANRAD 





1 


1 


1 


SU7BMKL 


HIFIX 





1 


1 





SU7U 








1 


1 





TA4EH 


APN15 3V 





1 








TA4KU 


APN153V 





1 








TORADV 


FOXHUNT 





1 





1 


TORIDS 


TI-ATG 


LASDES 


1 


1 


1 


TU16AG 


SHRTHRN 





1 





1 


TU22BD 


SHRTHRN 





1 





1 



168- 



ACFT 


NAAMR 


AAMR 


NAAMI 


AAMI 


GUN 


PGMC 


SA 


HUD 


CR 


ALPHAMS1 








2 


R550 





1 


1 








ALPHAMS2 








2 


R550 


DEFA553 


1 


1 


1 





AMX 








2 


AIM9PN 


M61A1 


1 


1 


1 


1 


A10A 














GAU8A 


1 


1 


1 





A37B 














GAU2BA 














A4H 








2 


SHAFRIR 


DEFA552A 


1 


1 


1 


1 


A4KU 








2 


AIM9PN 


DEFA55 3 


1 


1 


1 





A4N 








2 


AIM9L 


DEFA554 


1 


1 


1 


1 


A7E 








2 


AIM9L 


M61A1 


1 


1 


1 


1 


A7P 








2 


AIM9PN 


M61A1 


1 


1 


1 





BAC167 














FN7. 62 














CM170 














HGS55 














CM170I 














HGS55 














C101BB 














DEFA553 





1 








C101CC 














DEFA553 





1 








C101DD 














DEFA553 


1 


1 


1 


1 


FA18L 


2 


AIM7M 


2 


AIM9L 


M61A1 


1 


1 


1 


1 


F104GCF 








2 


AIM9E 


M61A1 














F14AC 


2 


AIM54 


2 


AIM9J 


M61A1 





1 


1 





F15A 


6 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 


1 





F15B 


6 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 


1 





F15C 


6 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 


1 





F15CFP 


6 


AIM7M 


2 


AIM9M 


M61A1 


1 


1 


1 





F15D 


6 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 


1 





F15E 


6 


AIM120A 


2 


AIM9M 


M61A1 


1 


1 


1 


1 


F16A 








4 


AIM9L 


M61A1 


1 


1 


1 


1 


F16B 








4 


AIM9L 


M61A1 


1 


1 


1 


1 


F16C 


2 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 


1 


1 


F16CSC 








4 


AIM9PN 


M61A1 


1 


1 


1 





F16D 


2 


AIM7F 


2 


AIM9L 


M61A1 


1 


I 


1 


1 


F16J79 








4 


AIM9PN 


M61A1 


1 


1 


1 





F20 








4 


AIM9PN 


M39 


1 


1 


1 





F20A 


2 


AIM7F 


2 


AIM9L 


M39 


1 


1 


1 





F4CD 


2 


AIM7E 


2 


AIM9D 





1 


1 








F4EF 


2 


AIM7F 


2 


AIM9L 


M61A1 


1 


1 





1 


F4M0D 


2 


AIM7M 


2 


AIM9M 


M61A1 


1 


1 


1 


1 


F5A 








2 


AIM9J 


M39 





1 








F5B 








2 


AIM9J 


M39 





1 








F5E 








2 


AIM9PN 


M39 


1 


1 








F5F 








2 


AIM9PN 


M39 


1 


1 








F86F 














US12. 7 














G91Y 














DEFA552A 


1 


1 


1 





HARMK80 








4 


AIM9L 


ADENMK5 


1 


1 


1 


1 


HAWK200 








2 


AIM9L 


ADENMK4 


1 


1 


1 


1 


HAWK50T 








2 


AIM9PN 


ADENMK4 





1 


1 





HAWK60A 








4 


AIM9PN 


ADENMK4 





1 


1 





HAWK60T 








2 


AIM9PN 


ADENMK4 





1 


1 





HUNTER 














ADENMK4 














HUNTERT 














ADENMK4 














IL28 














NR23 














JAGI04 








2 


AIM9PN 


ADENMK5 


1 


1 


1 





JAGI11 








2 


AIM9PN 


ADENMK5 


1 


1 


1 





JASTREB 














CB. 50 














KFIRC2 








4 


SHAFRIR 


DEFA553 


1 


1 


1 


1 


KFIRC7 








4 


PYTHON 3 


DEFA554 


1 


1 


1 


1 


KFIRTC2 








2 


SHAFRIR 


DEFA553 


1 


1 


1 


1 


LAVI 








4 


PYTHON3 


DEFA554 


1 


1 


1 


1 


LIGHTNG 








2 


REDTOP 


ADENMK4 





1 








L29 





























L39ZA 








2 


AA2B 


GSH23 














MB326K 








2 


R550 


DEFA552A 














MB326L 








2 


R550 

















MB339A 








2 


AIM9PN 








1 








MB339C 








2 


AIM9PN 


DEFA553 





1 


1 


1 


MB339K 








2 


AIM9PN 


DEFA553 





1 








MIG15BIS 














N37 















169 



MIG15UTI 














N37 














MIG17F 














N37D 














MIG19C 








2 


AA2B 


NR30 














MIG21C 


2 


AA2C 


2 


AA2B 


NR23 














MIG21F 


2 


AA2C 


2 


AA2B 


NR23HS 














MIG21JKL 


2 


AA2C 


2 


AA2D 


GSH23 





1 








MIG21R 








2 


AA2D 


GSH23 





1 








MIG21UM 


2 


AA2C 


2 


AA2B 


NR23HS 














MIG23B 


2 


AA7A 


2 


AA7B 


GSH23 


1 


1 


1 





MIG23E 


2 


AA2C 


2 


AA2D 


GSH23 


1 


1 








MIG23F 








2 


AA2D 


GSH23 


1 


1 








MIG23G 


2 


AA7A 


2 


AA8B 


GSH23 


1 


1 


1 





MIG23UM 


2 


AA2C 


2 


AA2D 


GSH23 


1 


1 


1 





MIG25 


2 


AA6A 


2 


AA6B 

















MIG25R 








2 


AA6B 








1 








MIG25U 


2 


AA6A 


2 


AA6B 

















MIG27DJ 








2 


AA8B 


NR30 


1 


1 


1 





MIG29 


4 


AA9A 


2 


AA8B 


NR30GAT 


1 


1 


1 


1 


MI631 


4 


AA9A 


2 


AA8B 


NR30GAT 





1 


1 





MIRF1A 








4 


R530I 


DEFA553 


1 


1 








MIRF1B 








4 


R530I 


DEFA553 


1 


1 








MIRF1C 


2 


SUP530F 


2 


R550 


DEFA55 3 


1 


1 








MIRF1E 


2 


SUP530F 


2 


R550 


DEFA55 3 


1 


1 








MIRIIIC 


2 


R530R 


2 


R530I 


DEFA552A 





1 


1 





MIRIIIE 


2 


R530R 


2 


R550 


DEFA552A 


1 


1 


1 





MIRIIIEI 


2 


R530R 


2 


SHAFRIR 


DEFA552A 


1 


1 


1 


1 


MIR2000C 


2 


SUP530D 


2 


R550MK2 


DEFA5 54 


1 


1 


1 


1 


MIR2000R 








2 


R550MK2 


DEFA554 


1 


1 


1 


1 


MIR2000T 


2 


SUP530D 


2 


R550MK2 


DEFA554 


1 


1 


1 





MIR3NG 


2 


SUP530F 


2 


R550MK2 


DEFA552A 


1 


1 


2 





MIR4000 


2 


SUP530D 


2 


R550MK2 


DEFA554 


1 


1 


1 


1 


MIR5DD 








2 


R550 


DEFA552A 





1 


1 





MIR5DR 








2 


R550 


DEFA552A 





1 


1 





MIR5D1 


2 


R530R 


2 


R550 


DEFA552A 





1 


1 





MIR5D1E 


2 


SUP530D 


2 


R550MK2 


DEFA552A 





1 


1 





MIR5D2 








2 


R550 


DEFA552A 


1 


1 


1 





OV10D 














M197 





1 








PRCA5 














NR30 





1 


1 





PRCFT6 














GSH23 














PRCF6 








2 


AA2B 


GSH23 














PRCF7 


2 


AA2C 


2 


AA2B 


GSH23 














PRCF7E 


2 


AA2C 


2 


AIM9PN 


GSH23 














RF4C 

















1 


1 








RF5E 














M39 


1 


1 








SF260MW 





























SF260TP 





























SUPETEN 








2 


R550 


DEFA553 


1 


1 


1 





SU20 








2 


AA2D 


NR30 





1 





1 


SU22 








2 


AA2D 


NR30 


1 


1 


• 


1 


SU25 














NR30GAT 


1 


1 


1 





SU27 


6 


AA10A 








NR30GAT 


1 


1 


1 


1 


SU7BMKL 








2 


AA2B 


NR30 














SU7U 














NR30 














TA4EH 








2 


SHAFRIR 


DEFA552A 





1 


1 


1 


TA4KU 








2 


AIM9E 


DEFA552A 


1 


1 


1 





TORADV 


4 


SKYFLASH 


4 


AIM9L 


MAU27 








1 





TORIDS 








2 


AIM9L 


ADENMK5 


1 


1 


1 


1 


TU16AG 














NR23 


1 











TU22BD 














NR23 


1 












170 



Appendix C 
AIRCREW SURVEY AND RELATIVE UTILITY VARIABLES 



C.l Aircrew Survey 



AIRFRAME COMPONENT 



1. What is the relative utility of the following airframe performance 
factors in achieving combat success in the roles indicated? 



Mission 




Air Defense 


Fighter 


Interdict: 


Ion ! 


Close Air 


Spt! 



Top Useful + Maneuver- + Combat = 100% 



Airspeed 



ability- 



Endurance 



PAYLOAD COMPONENT 

2. What is the relative utility of each of the listed weapons types ir 
achieving success in air defense and fighter missions respectively? 



Mission 


Air Defense! 


Fighter I 



Infrared + Radar Guided + GUN = 100% 



AAM 



AAM 



3. What is the relative utility of each of the listed weapons types ir 
achieving success in interdiction and CAS missions respectively? 



Mission 




Interdiction 


Close Air 


Spt. ! 



Freefall 
Munitions 



Guided + GUN = 100% 
Munitions 



- 171 - 



TARGET ACQUISITION COMPONENT 

4. What is the relative utility of each of the listed target 
acquisition methods in achieving success in the mission areas liste 
Assume that no more than 10% of the operations will be conducted at 
night, and that weather will not play a limiting role. Judge the 
situation as if all three types or target acquisition were availab. 



Mission 




Air Defense 


Fighter 


Interdiction 


Close Air 


Spt ! 



Visual + Radar + 



Other = 100% 
(IRSTS, LASER) 



VULNERABILITY TO ENGAGEMENT 

5. What is the utility of each of the following factors in reducir 
aircraft s susceptibility to engagement during each of the mission 
types? Consider size as a reciprocal measure (i.e. , the smaller 
better). 



Mission 




Air Defense 


Fighter 


Interdiction ! 


Close Air 


Spt ! 



Top Useful + Maneuver- + ECM 



Airspeed 



ability 



+ Size/ 

Signature 



= 100% 



- 172- 



AIR WEAPON SYSTEM 

6. What is the relative utility of each of the listed components in 
achieving mission success in each mission area? 



Mission 




Air Defense 


Fighter I 


Interdiction I 


Close Air 


Spt I 



Airframe + Target + Payload = 100% 
Acquisition 



EMPLOYMENT FACTORS 



7. What is the relative utility of each of the following factors in 
assuring the success of the missions listed? 



Mission 




Air Defense 


Fighter 


Interdiction ! 


Close Air 


Spt I 



Air Weapon + Operator 



System 



Proficiency 



+ C3I 
Support 



= 100% 



RESPONDANT INFORMATION 
8. Please provide information concerning the following: 



a. 
b. 
c. 
d. 
e. 



Current Aircraft: 
Aircrew Rating: 



Hours in Current Aircraft; 

Total Fighter Hours: 

Total Combat Hours: 



173- 



C.2 Survey Derived Relative Utility Values 



AIRFRAME COMPONENT 



Mission 




Air Defense 


Fighter 


Interdiction ! 


Close Air 


Spt ! 



Top Useful ! 
Airspeed 


.42 | 


. 30 1 


. 38 1 


.21 ! 



Maneuver- 
ability 



.29 



43 



26 



.38 



Combat 
Endurance 



. 29 


. 27 


. 36 


.41 



Mission 



Air Defense 



Fighter 



PAYLOAD COMPONENT 
Air-to Air Missions 



Infrared 

AAM 



.31 



. 39 



Radar Guided 
AAM 



.56 



. 39 



Air-to-Ground Missions 



GUN 



. 13 



.22 



Mission 




Interdiction 


Close Air 


Spt. 1 



Freefall 
Munitions 



. 38 



.28 



Guided 
Munitions ! 


.48 


.31 | 



GUN 



14 



.41 



TARGET ACQUISITION COMPONENT 



Mission 




Air Defense 


Fighter 


Interdiction ! 


Close Air 


Spt I 



Visual 



.20 


. 32 


. 39 


. 57 



Radar 



. 61 


. 51 


. 35 


. 13 



Other 
(IRSTS, LASER) 



. 17 


. 17 


.26 


. 30 



- 174 



VULNERABILITY TO ENGAGEMENT 



Mission 




Air Defense 


Fighter 


Interdiction 


Close Air 


Spt 1 



Top Useful ! 
Airspeed 


■ 


37 ! 


■ 


28 ! 


< 


35 ! 


■ 


19 j 



Maneuver- 
ability 



.26 


. 32 


.23 


.39 



ECM 



18 



18 



.23 



.20 



Size/ 
Signature 

. 19 

.22 



. 19 



.22 



AIR WEAPON SYSTEM 



Mission 




Air Defense 


Fighter 


Interdiction 


Close Air 


Spt ! 



Airframe 



.28 



. 33 



.27 



.27 



Target 
Acquisition 



.41 


. 37 


. 37 


.34 



Payload ! 


. 31 | 


. 30 j 


. 36 j 


. 39 ! 



EMPLOYMENT FACTORS 



Mission 




Air Defense 


Fighter 1 


Interdiction ! 


Close Air 


Spt 1 



Air Weapon 
System 


Operator 
Prof icien 


cy 


f C3I 
Support 


. 34 


.34 


. 32 


. 36 


•41 


.23 


. 39 


.41 


.20 


.36 


.43 


.21 



- 175- 



Appendix D 
MIDDLE EAST AIR ORDERS OF BATTLE 1984-1990 



ALGERIA 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


CM170 


CIN 


24 


20 


20 


20 


20 


20 


20 


MIG15BIS 


OCG 


4 




















MIG15UTI 


TNG 


20 


20 


20 


20 


20 


20 


20 


MIG17F 


FGA 


60 


60 


50 


40 


30 


20 





MIG17F 


TNG 


10 


10 


10 


10 


5 


5 





MIG21F 


FIN 


95 


95 


95 


95 


84 


72 


60 


MIG21UM 


OCA 


*8 


10 


10 


10 


10 


10 


10 


MIG23F 


FGA 


60 


60 


60 


60 


60 


60 


MIG23UM 


OCG 


2 


2 


2 


2 


2 


2 


2 


MIG25 


FIN 


18 


15 


15 


15 


15 








MIG25R 


REC 


4 


6 


6 


6 


6 


6 


6 


MIG25U 


OCA 


3 


3 


3 


3 


3 


3 


3 


MIG29 


FIN 














12 


24 


36 


MIG31 


FIN 

















18 


18 


SU20 


FGA 


18 


18 


18 


18 


18 


18 


18 


SU25 


FGA 











12 


12 


12 


12 


SU7BMKL 


FGA 


20 


12 


12 














0AR= . 6 


















MXRAT= 3. 


75 
















BAHRAIN 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


F5E 


FMR 


4 


6 


8 


8 


8 


8 


8 


F5F 


FMR 


2 


2 


4 


4 


4 


4 


4 


0AR= . 5 


















MXRAT= 2. 


5 

















- 176- 



20 


20 


26 


26 


32 


32 


6 


6 


17 


34 


6 


6 


16 











20 


10 








12 





8 





16 





36 


18 


15 


15 


21 


21 











17 


6 


6 


6 


3 


6 


6 


12 


16 


54 


47 


12 


18 


78 


78 


12 


12 


54 


72 














7 


7 



20 


20 


26 


26 


32 


32 


6 


6 


34 


34 


6 


6 












































18 


18 


15 


15 


6 


6 








34 


34 


6 


6 








6 


6 


16 


16 


24 


24 


22 


22 


78 


78 


12 


12 


72 


72 














7 


7 



EGYPT 

ACFT EMCODE 1984 1985 1986 1987 1988 1989 1990 

ALPHAMS1 TNG 8 20 

ALPHAMS2 FGA 15 19 26 

F16A FIN 34 32 32 

F16B OCA 6 6 6 

F16C FIN 

F16D OCA 6 

F4EF FMR 33 33 33 

IL28 REC 10 5 

L29 TNG 59 50 30 

MIG15UTI TNG 

MIG17F FGA 50 24 12 

MIG19C FIN 23 16 16 

MIG21F FIN 60 48 32 

MIG21JKL FIN 62 62 54 

MIG21R REC 15 15 15 

MIG21UM OCA 21 21 21 

MIG23E FIN 

MIR2000C FGA 

MIR2000T OCA 

MIR5DD OCA 6 6 6 

MIR5DR REC 6 6 6 

MIR5D1E FIN 6 

MIR5D2 FGA 46 50 54 

PRCFT6 OCA 4 4 4 

PRCF6 FGA 32 70 78 

PRCF6 FIN 12 12 12 

PRCF7 FIN 10 20 36 

SU20 FGA 

SU7BMKL FGA 20 20 

TU16AG BMR 111 
OAR= .6 
MXRAT= 4. 68 

ETHIOPIA 

ACFT EMCODE 1984 1985 1986 1987 1988 1989 1990 

F5B OCG 2 2 

F5E FGA 6 6 

L39ZA TNG 10 10 10 

MIG17F FGA 10 10 10 

MIG21F FGA 36 36 36 

MIG21JKL FGA 54 54 54 

MIG21UM OCG 10 10 10 

MIG23F FGA 20 35 38 

SF260TP TNG 4 4 8 

SU25 FGA 
OAR= . 4 
MXRAT= 2. 4 

IRAN 

ACFT EMCODE 1984 1985 1986 1987 1988 1989 1990 

F14AC FIN 25 20 

F4CD FMR 5 3 

F4EF FMR 30 20 

F5E FGA 40 32 

F5F FGA 10 7 

PRCF6 FGA 12 

RF4C REC 3 3 

RF5A REC 10 5 
OAR= . 6 
MXRAT= 22. 8 



177- 















10 


10 


10 





36 


36 


54 


54 


10 


10 


38 


38 


10 


10 





12 















10 


10 








24 


12 


54 


54 


10 


10 


38 


38 


10 


10 


24 


36 



15 


10 


6 


6 


6 

















20 


15 


10 


10 


10 


24 


16 


16 


16 


16 


5 


3 


3 


3 


3 


12 


12 


12 


12 


12 


3 


3 


3 


3 


3 


















IRAQ 
ACFT 


















EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


HUNTER 


TNG 


12 


12 


12 


12 


6 








HUNTERT 


TNG 


5 


5 


5 














IL28 


REC 























L29 


TNG 


12 


12 


6 














L39ZA 


TNG 


24 


24 


24 


24 


24 


24 


24 


MIG15UTI 


TNG 


30 


30 


30 


20 


10 








MIG17F 


FIN 


l°0 




















MIG19C 


FIN 


40 


20 














MIG21F 


FIN 


60 


60 


36 


24 











MIG21JKL 


FIN 


120 


140 


120 


108 


72 


60 


48 


MIG21UM 


OCA 


6 


6 


6 


6 


6 


6 


6 


MIG23E 


FIN 


48 


48 


60 


72 


84 


84 


84 


MIG23F 


FGA 


16 


18 


36 


36 


36 


36 


36 


MIG23UM 


OCA 


6 


6 


6 


6 


6 


6 


6 


MIG25 


FIN 


10 


17 


17 


17 


17 


17 


17 


MIG25R 


REC 


8 


8 


8 


8 


8 


8 


8 


MIG27DJ 


FGA 


6 


18 


36 


54 


54 


54 


54 


MIG29 


FIN 














12 


24 


36 


MIRF1B 


OCA 


4 


6 


8 


8 


8 


8 


8 


MIRF1C 


FGA 





8 


20 


20 


20 


20 


20 


MIRF1E 


FIN 


6 


8 


12 


24 


24 


24 


24 


PRCF7 


FIN 





25 


50 


75 


100 


100 


100 


SUPETEN 


FGA 


5 


5 


5 














SU20 


FGA 


45 


50 


60 


70 


80 


80 


80 


SU25 


FGA 











12 


24 


24 


24 


SU7BMKL 


FGA 


40 


40 


36 


18 











TU16AG 


BMR 


8 


6 


6 


6 


6 


6 


6 


TU22BD 


BMR 


7 


7 


7 


7 


7 


7 


7 


OAR= . 6 


















MXRAT= 6. 


68 
















ISRAEL 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


A4H 


FGA 


80 


80 


60 


36 


18 








A4N 


FGA 


50 


50 


50 


50 


50 


50 


50 


CM170I 


TNG 


85 


85 


85 


85 


85 


85 


85 


F15A 


FMR 


18 


18 


18 


18 


18 


18 


18 


F15B 


OCM 


2 


2 


2 


2 


2 


2 


2 


F15C 


FMR 


20 


20 


32 


32 


32 


32 


32 


F16A 


FMR 


62 


62 


62 


62 


62 


62 


62 


F16B 


OCM 


8 


8 


8 


8 


8 


8 





F16C 


FMR 











36 


54 


67 


67 


F16D 


OCM 








8 


8 


8 


8 


8 


F4EF 


FMR 


131 


131 


131 


115 


100 


84 


68 


KFIRC2 


FMR 


120 


120 


120 


120 


120 


120 


120 


KFIRC7 


FMR 





18 


36 


54 


72 


72 


72 


KFIRTC2 


TNG 


20 


30 


50 


60 


60 


60 


60 


MIRIIIEI 


FIN 























RF4C 


REC 


13 


13 


13 


13 


13 


13 


13 


TA4EH 


TNG 


73 


73 


73 


73 


73 


73 


73 


OAR= . 9 


















MXRAT= 7. 


75 

















- 178 



JORDAN 
ACFT 

C101DD 
F5A 
F5B 
F5E 
F5F 

HUNTERT 
MIRF1B 
MIRF1C 
MIRF1E 
OAR= . 8 
MXRAT= 5. 

KUWAIT 
ACFT 

A4KU 
BAC167 
HAWK60A 
HUNTER 
HUNTERT 
LIGHTNG 
MIRF1B 
MIRF1C 
TA4KU 
OAR= . 6 
MXRAT= 2. 

LEBANON 
ACFT 

HUNTER 
HUNTERT 
MIRIIIB 
MIRIIIE 
OAR=NA 
MXRAT=NA 

LIBYA 
ACFT 

JASTREB 
L39ZA 
MIG21F 
MIG23E 
MIG23F 
MIG23UM 
MIG25 
MIG25R 
MIG25U 
MIG29 
MIG31 
MIRF1A 
MIRF1B 
MIRF1E 
MIR5DD 
MIR5DR 
MIR5D2 
SF260MW 
SU22 
SU25 
TU22BD 
OAR= . 3 
MXRAT= 1. 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



CIN 
OCG 
OCG 
FGA 
FGA 
TNG 
OCA 
FIN 
FIN 



71 



FGA 
TNG 
CIN 
FGA 
OCA 
FIN 
OCA 
FIN 
OCG 



25 



FGA 
OCG 
OCM 

FMR 



CIN 
TNG 

FIN 
FIN 
FGA 
OCA 
FIN 
REC 
OCA 
FIN 
FIN 
FGA 
OCA 
FIN 
OCG 
REC 
FGA 
TNG 
FGA 
FGA 
BMR 

27 




17 

5 
57 
12 

3 

2 
15 
17 




15 

5 
56 
12 



2 
19 
17 




15 

5 
56 
12 



2 
19 
17 



6 
15 

5 
56 
12 



2 
19 
17 



12 

7 

5 

56 

12 



2 

19 

17 



14 

7 

7 

56 

12 



2 

19 

17 



30 
9 

6 
3 

2 

17 
6 



28 
9 

12 



2 

32 
6 



28 
9 

12 



2 

41 
6 



28 
9 

12 



2 

41 
6 



28 
9 

12 



2 

41 
6 



28 
9 

12 



2 

41 
6 



3 
2 





3 





































30 

30 

30 

100 

18 

14 

50 

7 

5 





14 

6 

26 

13 

7 

45 

100 

100 



9 



30 

30 

30 

124 

36 

14 

55 

7 

5 





14 

6 

26 

13 

7 

43 

120 

100 



9 



30 

30 

15 

143 

36 

14 

60 

7 

5 





14 

6 

40 

13 

7 

43 

140 

100 



9 



30 

30 



143 

36 

14 

60 

7 

5 





14 

6 

54 

13 

7 

43 

160 

100 



9 



30 

30 



124 

36 

14 

60 

7 

5 

12 





6 

66 

13 

7 

43 

170 

100 

12 

9 



30 
30 



112 

36 

14 

48 

7 

5 
24 
12 



6 
66 
13 

7 

43 

170 

100 

12 

9 



14 

7 

7 

56 

12 



2 

19 

17 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



28 
9 

12 



2 

41 
6 



EMCODE 1984 1985 1986 1987 1988 1989 1990 











EMCODE 1984 1985 1986 1987 1988 1989 1990 



30 
30 



100 

36 

14 

36 

7 

5 
24 
24 



6 
66 
13 

7 

43 

170 

100 

12 

9 



179- 



MOROCCO 
ACFT 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



ALPHAMS1 TNG 

CM170 CIN 

F5A FGA 

F5B OCG 

F5E FGA 

F5F FGA 

MIRF1C FGA 

MIRF1E FGA 

OV10D CIN 

RF5A REC 

SF260MW TNG 

OAR= . 6 

MXRAT= 8.28 



24 

22 

5 

■J 

18 
22 
6 
12 
28 



24 
22 

5 
3 

l S 

18 
21 
6 
12 
28 



24 
22 

5 

3 
14 

4 
18 
21 

6 
12 
28 



24 
22 

5 

3 
14 

4 
18 
21 

6 
12 
28 



24 
22 

5 

3 
14 

4 
18 
21 

6 
12 
28 



24 
22 

5 

3 
14 

4 
18 
21 

6 
12 
28 



OMAN 
ACFT 



BAC167 CIN 

HUNTER FGA 

HUNTERT OCG 

JAGI11 FGA 

TORADV FIN 

OAR= . 7 

MXRAT= 5. 73 



12 
12 

4 
24 





12 
12 

4 
24 





12 
12 

2 
24 





12 
6 


24 
4 



12 
6 


24 
8 



12 
6 


24 
8 



QATAR 
ACFT 



ALPHAMS2 FGA 
HUNTER FGA 
HUNTERT OCG 
MIRF1B OCG 
MIRF1C FGA 

OAR= . 6 

MXRAT= 1. 43 

SAUDI ARABIA 
ACFT EMCODE 

BAC167 TNG 

F15C FIN 

F15D OCA 

F5B OCG 

F5E FGA 

F5F FGA 

HAWK60T TNG 

LIGHTNG FIN 

RF5E REC 

TORADV FIN 

TORIDS FGA 

OAR= . 7 

MXRAT= 6.03 



6 
3 
1 

5 



8 
2 
1 

10 



8 
2 

2 
12 



8 


2 
12 



8 


2 
12 



8 


2 
12 



24 
22 

5 

3 
14 

4 
18 
21 

6 
12 
28 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



12 
6 


24 
8 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



8 


2 
12 



1984 1985 1986 1987 1988 1989 1990 



40 


40 


40 


20 











46 


54 


54 


54 


54 


54 


54 


15 


16 


17 


17 


17 


17 


17 


16 


16 


16 


16 


16 


16 


16 


65 


65 


70 


70 


54 


36 


36 


24 


24 


25 


25 


25 


25 


25 











15 


30 


30 


30 


17 


17 


16 




















10 


10 


10 


10 


10 














12 


24 


24 











20 


36 


48 


48 



180- 



SOMALIA 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


HUNTER 


FGA 


10 


10 


10 


10 


10 


10 


10 


HUNTERT 


OCG 


2 


2 


2 


2 


2 


2 


2 


MIG15UTI 


TNG 


2 


2 


2 


2 


2 


2 


2 


MIG17F 


FGA 


9 


9 


9 


9 


9 


9 


9 


MIG21F 


FMR 


7 


7 


7 


7 


7 


7 


7 


PRCF6 


FMR 


30 


30 


30 


30 


30 


30 


30 


SF260MW 


TNG 


4 


4 


4 


4 


4 


4 


4 


SF260MW 


CIN 


6 


6 


6 


6 


6 


6 


6 


OAR= . 4 


















MXRAT= 2. 


86 
















SUDAN 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


BAC167 


CIN 


3 


3 


7 


10 


10 


10 


10 


F5E 


FMR 


2 


2 


6 


10 


10 


10 


10 


F5F 


FMR 


2 


2 


2 


2 


2 


2 


2 


MIG17F 


FGA 


10 


10 


10 


6 


3 








MIG21F 


FMR 


8 


8 


8 


8 


8 


8 


8 


MIG21UM 


OCM 


2 


2 


2 


2 


2 


2 


2 


PRCFT6 


OCA 


2 


2 


2 


2 


2 


2 


2 


PRCF6 


FGA 


6 


6 


9 


12 


12 


12 


12 


OAR= . 4 


















MXRAT= 8. 


57 
















SYRIA 


















ACFT 


EMCODE 


1984 


1985 


1986 


1987 


1988 


1989 


199 


IL28 


BMR 























L29 


TNG 


60 


60 


60 


60 


60 


60 


60 


L39ZA 


TNG 


40 


40 


40 


40 


40 


40 


40 


MIG15UTI 


TNG 


10 


10 


10 


10 


10 


10 


10 


MIG17F 


FGA 


85 


85 


67 


49 


36 


18 





MIG21F 


FIN 


92 


92 


92 


84 


72 


36 





MIG21JKL 


FIN 


100 


108 


108 


96 


84 


72 


36 


MIG21UM 


OCA 


20 


20 


20 


20 


20 


20 


20 


MIG23B 


FIN 


24 


24 


24 


24 


24 


24 


24 


MIG23E 


FIN 


24 


24 


24 


36 


48 


60 


72 


MIG23F 


FGA 


50 


50 


60 


70 


70 


70 


70 


MIG23G 


FIN 





36 


36 


36 


36 


36 


36 


MIG23UM 


OCG 


10 


10 


10 


10 


10 


10 


10 


MIG25 


FIN 


25 


25 


30 


38 


38 


38 


38 


MIG25R 


REC 


3 


6 


10 


12 


12 


12 


12 


MIG29 


FIN 











12 


24 


36 


72 


SU22 


FGA 


40 


42 


42 


42 


42 


42 


42 


SU25 


FGA 











12 


24 


36 


36 


SU27 


FIN 




















24 


SU7BMKL 


FGA 


36 


36 


24 


12 











SU7U 


OCG 


2 


2 


2 














OAR= . 7 


















MXRAT= 10 


.45 

















181 



TUNISIA 
ACFT 

F5E 
F5F 

MB326K 
MB326K 
MB326L 
SF260MW 
OAR= 5. 68 
MXRAT= . 6 



EMCODE 1984 1985 1986 1987 1988 1989 1990 



FGA 
OCG 

CIN 
TNG 
CIN 
TNG 





5 
7 
3 
17 



UNITED ARAB EMIRATES 
ACFT EMCODE 1984 



ALPHAMS2 FGA 
HAWK50T TNG 
HAWK60A FGA 
HAWK60T OCG 
HUNTER FGA 
HUNTERT OCA 
MB326K CIN 
MB326L CIN 
MB339A TNG 
MIRIIIE FIN 
MIR2000C FIN 
MIR2000R REC 
MIR2000T OCA 
MIR5DD OCA 
MIR5DR REC 
MIR5D1 FIN 
SF260TP TNG 

OAR= . 6 

MXRAT= 2. 83 

NORTH YEMEN 
ACFT EMCODE 

F5B TNG 
F5E FMR 
IL28 BMR 
MIG15UTI TNG 
MIG17F FMR 
MIG21F FMR 
SU22 FGA 

OAR= . 4 

MXRAT= 1. 23 

SOUTH YEMEN 
ACFT EMCODE 

MIG15UTI TNG 
MIG17F FIN 
MIG21F FIN 
MIG21JKL FGA 
MIG21UM OCA 
MIG23E FIN 
MIG29 FIN 
SU22 FGA 
SU25 FGA 

OAR= . 5 

MXRAT=2. 34 



8 

4 
5 
7 
3 
17 



8 
6 
5 
7 
3 
17 



8 
6 
5 
7 
3 
17 



8 
6 
5 
7 
3 
17 



8 
6 
5 

7 

3 

17 



8 
6 
5 
7 
3 
17 



1985 1986 1987 1988 1989 1990 



3 


3 


6 


6 


6 


6 


6 


3 


3 


3 


3 


3 


3 


3 











8 


16 


16 


16 








8 


8 


8 


8 


8 












































5 


5 


5 


5 


5 


5 


5 


5 


5 


5 


5 


I 


I 


5 


2 


2 


4 


4 


4 
































12 


24 


32 


32 











3 


3 


3 


3 








3 


3 


3 


3 


3 


2 


2 


2 














3 


il 


3 














25 


24 


12 











6 


6 


6 


6 


6 


6 


6 



1984 1985 1986 1987 1988 1989 1990 



4 


4 


4 


4 


4 


4 


4 


10 


8 


8 


8 


8 


8 


8 








I 








I 





4 


4 


4 


4 


4 


10 


10 


10 


10 


10 


10 


10 


40 


40 


40 


40 


40 


40 


40 


15 


15 


15 


15 


15 


15 


15 



1984 1985 1986 1987 1988 1989 1990 



3 


3 


3 


3 


3 


3 


3 


30 


30 


30 


30 


18 








36 


36 


36 


24 


12 








12 


12 


12 


12 











1 


1 


1 


1 


1 


1 


1 











12 


24 


36 


36 














12 


24 


36 


25 


25 


25 


25 


25 


25 


25 














12 


12 


12 



- 182 



Appendix E 
AIR WEAPON SUBSYSTEM FACTOR SCORES 



E.l Airframes 



Glossary 



NFSS = Speed/Energy Factor Score 

NFSM = Maneuverability Factor Score 

NFSRA = Air-to-Air Range /Endurance Factor Score 

NFSRG = Air-to-Ground Range /Endurance Factor Score 

NFSO = Air-to-Ground Ordnance Factor Score 

NRND = Indexed Gun Ordnance Capacity 

NFSV = Size/ Signature Factor Score 



ACFT 



ROLE 



NFSS NFSM NFSRA NFSRG 



NFSO 



NRND 



NFSV 



ALPHAMS1 


FTTC 


• 


601 


1. 


041 




000 


1. 


115 


« 


834 




000 




689 


ALPHAMS2 


FTAT 


• 


601 


1. 


034 


■ 


000 


1. 


115 


. 


834 




382 




694 


AMX 


FTAT 


• 


783 


• 


927 




000 


1. 


087 




959 


1. 


069 




813 


A10A 


FTAT 


• 


356 


1. 


023 




000 


1. 


088 


1. 


813 


3. 


587 


l! 


546 


A37B 


FTAT 


■ 


413 


. 


890 


• 


000 


• 


546 


, 


960 




611 




761 


A4H 


FTAT 


. 


604 


, 


902 


, 


000 


1. 


482 


, 


969 


L 


222 




763 


A4KU 


FTAT 


• 


595 


, 


902 


• 


000 




937 


• 


969 


1. 


222 




763 


A4N 


FTAT 


• 


636 


• 


934 


• 


000 


1. 


022 


1. 


244 




917 




773 


A7E 


FTAT 


• 


742 


. 


821 


, 


000 


1. 


537 


1. 


367 


3! 


153 


l! 


151 


A7P 


FTAT 


■ 


637 


• 


782 




000 


1. 


537 


1. 


335 


3. 


153 


l. 


153 


BAC167 


FTAT 


• 


375 


« 


771 


, 


000 


• 


778 


• 


605 




611 




759 


CM170 


FTTC 


• 


311 


, 


896 


. 


000 


, 


539 


, 


252 


1. 


100 




776 


CM170I 


FTTC 


• 


311 


. 


896 


, 


000 


, 


539 


, 


252 


1. 


100 




776 


C101BB 


FTAT 


. 


353 


, 


953 


. 


000 




941 


, 


928 


, 


611 




784 


C101CC 


FTAT 


• 


377 


. 


986 


• 


000 


1. 


020 


, 


928 




611 




784 


C101DD 


FTTA 


, 


377 




986 




000 


1. 


020 




928 




611 




784 


FA18L 


FTMR 


1. 


466 


1. 


252 


l! 


303 


1. 


381 


1. 


692 


1. 


741 


l! 


114 


F104GCF 


FTAT 


1. 


373 


1. 


031 




000 


, 


725 


1. 


158 


2. 


215 




664 


F14AC 


FTIN 


1. 


257 


1. 


054 


l! 


563 




000 




000 


2. 


062 


l! 


469 


F15A 


FTMR 


1. 


506 


1. 


236 


l. 


358 


1. 


418 


1. 


271 


2. 


872 


i. 


451 


F15B 


FTTM 


1. 


506 


1. 


225 


l. 


303 


1. 


344 


1. 


271 


2. 


872 


l. 


459 


F15C 


FTMR 


1. 


506 


1. 


380 


l. 


466 


1. 


563 


1. 


271 


2. 


872 


l. 


460 


F15CFP 


FTMR 


1. 


261 


1. 


322 


l. 


717 


1. 


830 


1. 


530 


2. 


872 


i. 


504 


F15D 


FTTM 


1. 


506 


1. 


370 


l. 


411 


1. 


511 


1. 


271 


2. 


872 


i. 


467 


F15E 


FTMR 


1. 


468 


1. 


480 


l. 


542 


1. 


606 


2. 


095 


2. 


872 


l. 


450 


F16A 


FTMR 


1. 


462 


1. 


386 


l. 


168 


1. 


225 


1. 


495 


1. 


573 


, 


870 


F16B 


FTTM 


1. 


462 


1. 


384 


l. 


113 


1. 


183 


1. 


495 


1. 


573 


, 


871 


F16C 


FTMR 


1. 


462 


1. 


312 


l. 


113 


1. 


225 


1. 


495 


1. 


573 




900 


F16CSC 


FTMR 


1. 


462 


1. 


326 


l. 


113 


1. 


225 


1. 


495 


1. 


573 




894 


F16D 


FTTM 


1. 


462 


1. 


298 


l. 


069 


1. 


194 


1. 


495 


1. 


573 




907 


F16J79 


FTMR 


1. 


302 


1. 


192 


. 


835 




840 


1. 


353 


1. 


573 


. 


889 


F20 


FTMR 


1. 


355 


1. 


357 




885 


• 


993 


1. 


193 


2. 


750 




667 


F20A 


FTMR 


1. 


355 


1. 


334 




885 




993 


1. 


193 


2. 


750 




673 


F4CD 


FTMR 


1- 


323 


1. 


050 




921 


1. 


009 


1. 


411 




000 


l! 


323 


F4EF 


FTMR 


1. 


350 


1. 


049 




844 


• 


873 


1. 


546 


l! 


952 


i. 


346 


F4M0D 


FTMR 


1. 


350 


1. 


087 


l! 


185 


1. 


111 


2. 


077 


l. 


952 


i. 


366 


F5A 


FTMR 


, 


940 


1. 


025 




642 




634 




864 




855 




592 


F5B 


FTMR 


• 


931 


1. 


018 


, 


642 


. 


634 


. 


864 


# 


855 


. 


594 



184 



F5E 


FTMR 


1.065 


1. 


036 




756 




778 




, 899 


. 855 




644 


F5F 


FTMR 


1. 035 


1. 


Oil 


i 


626 




638 




, 899 


. 855 




, 654 


F86F 


FTMR 


.837 




727 


t 


676 




685 




, 325 


. 611 




884 


G91Y 


FTAT 


. 699 




960 


■ 


000 


1. 


007 




, 649 


. 611 




, 695 


HARMK80 


FTMR 


. 856 


l! 


311 


1. 


067 


1. 


111 




, 943 


. 764 




, 675 


HAWK200 


FTMR 


. 584 




963 


1. 


184 


1. 


140 




, 890 


. 917 




, 684 


HAWK50T 


FTTA 


. 625 




952 




000 




897 




,423 


. 367 




, 681 


HAWK60A 


FTAT 


. 651 


■ 


932 


l! 


074 


l! 


087 




, 890 


. 367 




, 705 


HAWK60T 


FTTA 


. 651 


, 


968 


l. 


074 


l. 


087 




, 660 


. 367 




681 


HUNTER 


FTMR 


. 798 




925 


l. 


032 




973 




, 785 


. 611 




904 


HUNTERT 


FTTM 


. 798 


, 


915 


l. 


071 




983 




785 


. 611 




912 


IL28 


BMAT 


. 392 


, 


503 




000 


l! 


449 


1, 


, 001 


1. 986 


L 


927 


JAGI04 


FTAT 


1. 108 


l. 


108 




000 


l. 


165 


1. 


, 171 


. 917 




843 


JAGI11 


FTAT 


1. 124 


l. 


140 




000 


l. 


165 


1. 


171 


. 917 




843 


JASTREB 


FTAT 


. 378 


• 


852 




000 


, 


477 




817 


1. 237 




742 


KFIRC2 


FTMR 


1.457 


l. 


058 


l! 


080 


l. 


199 


l! 


202 


. 855 




875 


KFIRC7 


FTMR 


1.457 


l. 


081 


l. 


157 


i. 


204 


l. 


366 


. 855 




874 


KFIRTC2 


FTTM 


1.457 


i. 


052 


■ 


949 


l. 


074 


i. 


, 202 


. 855 




878 


LAVI 


FTMR 


1.026 


l. 


287 


• 


798 


, 


724 


l. 


448 


. 855 




873 


LIGHTNG 


FTIN 


1.432 


l. 


076 




904 


, 


000 




, 000 


. 733 


L 


125 


L29 


FTTA 


.267 


• 


782 


i 


000 


■ 


359 




256 


. 611 




856 


L39ZA 


FTTC 


. 310 


■ 


774 


■ 


528 


■ 


553 




580 


.458 


i 


, 703 


MB326K 


FTAT 


.447 


■ 


692 


■ 


000 




570 




886 


. 611 




721 


MB326L 


FTTA 


.447 


• 


692 


• 


000 


, 


570 




886 


. 611 




721 


MB339A 


FTTC 


.501 


■ 


709 


• 


657 


■ 


625 




886 


. 000 




770 


MB339C 


FTAT 


.495 


■ 


720 


• 


000 


• 


761 




, 898 


. 855 




, 787 


MB339K 


FTAT 


.495 


• 


720 


« 


000 


, 


761 




898 


. 855 




787 


MIG15BIS 


FTMR 


. 651 




817 


■ 


600 


■ 


576 


■ 


, 325 


.489 




, 767 


MIG15UTI 


FTTC 


. 629 


. 


815 




469 


■ 


421 




, 000 


. 611 




735 


MIG17F 


FTMR 


. 619 


, 


847 


■ 


628 


, 


620 




, 309 


. 611 




829 


MIG19C 


FTMR 


.837 


l. 


104 


, 


726 


, 


652 


■ 


364 


. 611 




790 


MIG21C 


FTIN 


. 931 


l. 


074 


■ 


700 


■ 


000 


■ 


000 


. 611 




681 


MIG21F 


FTMR 


1.081 


l. 


103 


• 


716 


m 


645 




548 


. 611 




680 


MIG21JKL 


FTMR 


1. 161 


l. 


122 


■ 


700 


, 


563 




548 


. 611 




681 


MIG21R 


FTRE 


1.081 


l. 


095 


• 


000 


• 


711 




000 


. 611 




683 


MIG21UM 


FTTM 


1. 155 


l. 


109 


■ 


703 


, 


637 


i 


548 


. 611 




687 


MIG23B 


FTMR 


1.459 


l. 


056 


, 


923 


. 


955 




667 


. 611 


l! 


270 


MIG23E 


FTMR 


1.455 


l. 


079 


• 


923 


• 


955 


i 


667 


. 611 


l. 


252 


MIG23F 


FTAT 


1. 206 


• 


862 


• 


000 


• 


918 


■ 


667 


. 611 


l. 


293 


MIG23G 


FTMR 


1.468 


l. 


067 


• 


923 




955 




667 


. 611 


l. 


262 


MIG23UM 


FTTC 


1. 369 


. 


997 




814 


. 


824 




667 


. 611 


l. 


264 


MIG25 


FTIN 


1. 520 


. 


897 


1. 


044 




000 




000 


. 000 


l. 


738 


MIG25R 


FTRE 


1. 554 


• 


912 




000 


1. 


019 


■ 


000 


. 000 


l. 


676 


MIG25U 


FTTI 


1. 520 


. 


897 


1. 


022 


. 


000 




000 


. 000 


l. 


737 


MIG27DJ 


FTAT 


1.090 


. 


881 


. 


000 


. 


975 




882 


1. 528 


l. 


299 


MIG29 


FTMR 


1.582 


l. 


361 


, 


798 


. 


887 


L 


096 


. 611 


l. 


097 


MIG31 


FTIN 


1. 566 


. 


997 


1. 


263 




000 




000 


1. 100 


l. 


697 


MIRF1A 


FTMR 


1.400 


l. 


006 


1. 


205 


l! 


062 




979 


. 825 




812 


MIRF1B 


FTTI 


1.400 


l. 


006 


1. 


172 




000 




000 


. 825 




812 


MIRF1C 


FTMR 


1.400 


l. 


009 


1. 


205 


l! 


104 




979 


. 825 




810 


MIRF1E 


FTMR 


1. 734 


. 


990 


1. 


315 


l. 


213 




979 


. 825 




824 


MIRIIIC 


FTIN 


1. 144 


• 


962 


1. 


037 




000 




000 


. 764 




858 


MIRIIIE 


FTMR 


1. 172 


• 


965 


1. 


292 


l! 


150 




978 


. 764 




862 


MIRIIIEI 


FTMR 


1. 172 




965 


1. 


292 


l. 


150 


l! 


216 


. 764 




863 


MIR2000C 


FTMR 


1. 563 


l. 


217 


, 


979 


l. 


056 


1. 


437 


. 764 




979 


MIR2000R 


FTRE 


1. 513 


l. 


082 


, 


000 


l. 


252 




000 


. 611 




969 


MIR2000T 


FTTM 


1. 563 


l. 


206 


, 


957 


l. 


035 


l! 


437 


. 764 




985 


MIR3NG 


FTMR 


1. 169 




990 


1. 


359 


l. 


483 


l. 


235 


. 764 




891 


MIR4000 


FTMR 


1.467 


l. 


334 


1. 


519 


l. 


252 


l. 


838 


. 611 


l! 


535 


MIR5DD 


FTTA 


1.240 


. 


925 


. 


000 


l. 


382 




943 


. 764 




898 


MIR5DR 


FTRE 


1. 240 




928 




000 


l. 


521 




000 


. 764 




894 


MIR5D1 


FTIN 


1.244 


• 


960 


l! 


238 




000 




000 


. 764 




866 


MIR5D1E 


FTIN 


1. 244 


• 


958 


l. 


238 




000 




000 


. 764 




867 


MIR5D2 


FTAT 


1. 240 


. 


926 


• 


000 


l! 


521 




998 


. 764 




896 


OV10D 


MIAT 


.213 




787 


■ 


000 




659 


• 


750 


3. 055 




897 


PRCA5 


FTAT 


. 920 


. 


846 


. 


000 


, 


759 


, 


786 


1. 528 




881 


PRCFT6 


FTTM 


. 967 




935 


• 


725 




641 


• 


000 


. 611 




784 


PRCF6 


FTMR 


. 943 


# 


962 


# 


725 


• 


693 


f 


000 


. 611 


, 


789 



185 



PRCF7 


FTIN 


. 861 


1.074 


. 700 


. 000 


.000 


. 611 


. 681 


PRCF7E 


FTIN 


. 861 


1.096 


. 700 


. 000 


.000 


. 611 


. 680 


RF4C 


FTRE 


1. 318 


1.053 


. 000 


1.047 


. 000 


. 000 


1. 320 


RF5E 


FTRE 


1.052 


1.011 


. 000 


. 861 


. 000 


. 855 


. 654 


SF260MW 


MIT A 


. 104 


1.041 


. 000 


. 610 


. 503 


. 000 


. 502 


SF260TP 


MIT A 


. 182 


. 796 


.000 


. 610 


. 503 


. 000 


. 500 


SUPETEN 


FTAT 


.842 


. 854 


. 000 


1. 016 


. 914 


. 764 


. 878 


SU20 


FTAT 


1. 318 


. 937 


. 000 


. 801 


1. 334 


.428 


1. 289 


SU22 


FTAT 


1. 318 


. 930 


. 000 


. 935 


1.431 


.428 


1. 305 


SU25 


FTAT 


. 369 


1.074 


.000 


.860 


1. 571 


. 611 


1. 338 


SU27 


FTMR 


1.480 


1. 389 


1. 292 


. 913 


1.290 


. 611 


1. 534 


SU7BMKL 


FTAT 


. 781 


. 934 


.000 


.559 


. 715 


.428 


. 885 


SU7U 


FTTA 


. 784 


. 935 


. 000 


.480 


. 715 


.428 


. 884 


TA4EH 


FTTA 


. 612 


. 894 


. 000 


1. 169 


. 951 


1.222 


. 761 


TA4KU 


FTTA 


.595 


. 907 


. 000 


.813 


. 951 


1.222 


. 768 


TORADV 


FTIN 


1. 342 


1. 068 


1. 387 


.000 


.000 


. 611 


1. 355 


TORIDS 


FTAT 


1. 308 


1.009 


. 000 


1.554 


1. 935 


. 611 


1.415 


TU16AG 


BMAT 


. 586 


. 523 


. 000 


2. 740 


1. 814 


. 611 


4. 187 


TU22BD 


BMAT 


.877 


.571 


.000 


2. 924 


2. 342 


. 611 


3. 834 



186 



E.2 Target Acquisition Systems 



Glossary 



NFSTA = Target Acquisition Effectiveness Factor Score 
NAME CODE NFSTA 



AGAVE 


RAMU 


• 


742 


AIDAII 


RAGA 


• 


360 


AIRPASSI 


RAAI 


1. 


124 


ANTILOPE 


RAMU 


1. 


432 


APG63 


RAMU 


1. 


880 


APG64 


RAMU 


2. 


021 


APG65 


RAMU 


1. 


374 


APG66 


RAMU 


1. 


176 


APG67 


RAMU 


1. 


480 


APG68 


RAMU 


1. 


445 


APG69 


RAMU 




910 


APG70 


RAMU 


2. 


039 


APN153V 


RAGA 


• 


596 


APQ109 


RAMU 


• 


740 


APQ120 


RAMU 


• 


777 


APQ159 


RAAI 


• 


678 


AWG9 


RAAI 


2. 


189 


BLUEFOX 


RAMU 


1. 


094 


CYRI 


RAAI 




798 


CYRII 


RAMU 


• 


894 


CYRIV 


RAAI 


1. 


000 


CYRIVM3 


RAMU 


1. 


094 


CYRIV 2 


RAMU 


1. 


094 


ELM2001B 


RAMU 




691 


ELM2021B 


RAMU 


1. 


079 


ELTAFIAR 


RAGA 


• 


762 


FLANRAD 


RAMU 


1. 


982 


FOXFIRE 


RAAI 


1. 


214 


FOXHUNT 


RAMU 


2. 


042 


FULRAD 


RAMU 


1. 


092 


HIFIX 


RAMU 


• 


385 


HILARKI 


RAMU 


• 


882 


HILARKII 


RAMU 


1. 


050 


HILARKX 


RAAI 


1. 


233 


HOUNDRAD 


RAAI 


1. 


928 


IRSTSB 


IRAI 




491 


IRSTSG 


IRAI 


« 


614 


JAYBIRD 


RAAI 


• 


733 


LASDES 


LAGA 


■ 


349 


LASRNG 


LAGA 


* 


316 


RDA12 


RAGA 


, 


488 


RDI 


RAAI 


1. 


355 


RDM 


RAMU 


1. 


379 


SCANFIX 


RAAI 


• 


450 


SCANODD 


RAAI 


• 


458 


SHRTHRN 


RAGA 


• 


762 


SKYRNGR 


RAAI 


• 


568 


SPNSCNA 


RAAI 


4 


484 


SPNSCNB 


RAAI 


. 


484 


TI-ATA 


RAMU 


1. 


160 


TI-ATG 


RAMU 


1. 


355 


VISUAL 


VIMU 


■ 


275 



187- 



E.3 Air-to-Air Missiles 



Glossary 



NFSPERF = Missile Performance Factor Score 

NFSVUL = Vulnerability to Detection/Avoidance Factor Score 



MSL 


CODE 


NFSPERF 


NFSVUL 


AA10A 


AAMR 


1. 


28 


.86 


AA2B 


AAMI 


• 


54 


. 75 


AA2C 


AAMR 


• 


65 


. 68 


AA2D 


AAMI 




65 


. 68 


AA6A 


AAMR 


1. 


23 


2.21 


AA6B 


AAMI 


1. 


14 


2.21 


AA7A 


AAMR 


1. 


33 


1.49 


AA7B 


AAMI 


1. 


25 


1.47 


AA8B 


AAMI 


( 


63 


. 65 


AA9A 


AAMR 


1. 


68 


1.46 


AIM120A 


AAMR 


1. 


35 


.86 


AIM54 


AAMR 


3. 


21 


1. 76 


AIM7C 


AAMR 


1. 


28 


1.24 


AIM7D 


AAMR 


1. 


28 


1.27 


AIM7E 


AAMR 


1. 


36 


1. 15 


AIM7F 


AAMR 


1. 


94 


1. 17 


AIM7M 


AAMR 


1. 


94 


1. 17 


AIM9D 


AAMI 


* 


74 


. 72 


AIM9E 


AAMI 


• 


49 


. 70 


AIM9G 


AAMI 


• 


74 


. 71 


AIM9H 


AAMI 


• 


74 


. 71 


AIM9J 


AAMI 


• 


64 


. 63 


AIM9L 


AAMI 


• 


86 


. 64 


AIM9M 


AAMI 


• 


86 


. 64 


AIM9PN 


AAMI 




69 


. 63 


ASPIDE 


AAMR 


1. 


48 


1. 34 


FIRESTRK 


AAMI 


• 


81 


1. 11 


KUKRI 


AAMI 


■ 


39 


. 63 


PIRANHA 


AAMI 


• 


55 


.81 


PYTH0N3 


AAMI 


• 


65 


. 76 


RBS70 


AAMI 


• 


28 


. 65 


REDTOP 


AAMI 


• 


92 


1. 12 


R530I 


AAMI 


• 


80 


1. 15 


R530R 


AAMR 


■ 


80 


1. 15 


R550 


AAMI 


• 


74 


. 83 


R550MK2 


AAMI 


• 


80 


. 76 


SHAFRIR 


AAMI 


• 


57 


.84 


SKYFLASH 


AAMR 


1. 


30 


1. 32 


STINGER 


AAMI 


• 


33 


. 68 


SUP530D 


AAMR 


1. 


72 


1. 18 


SUP530F 


AAMR 


1. 


35 


1.21 



- 188- 



E.4 Aerial Guns 



Glossary 



NFSRAT = Rate/Volume of Fire Factor Score 
NFSEFF = Effectiveness Factor Score 



GUN 

ADENMK4 

ADENMK5 

CB. 50 

DEFA552A 

DEFA553 

DEFA554 

FN7. 62 

GAU12U 

GAU13A 

GAU2BA 

GAU8A 

GPU5A 

GSH23 

HGS55 

HIS404 

KCA30 

MAU27 

MKIIM0D5 

M16 

M197 

M230 

M28 

M3 9 

M5 

M61A1 

M621 

NR23 

NR23HS 

NR30 

NR30GAT 

N37 

N37D 

UBK 

US12. 7 

XM188E30 

XM27E1 

XM8 



CODE NFSRAT NFSEFF 



ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCE 
ACCE 
ACCI 
ACCE 
ACCI 
ACCE 
ACCI 
ACCE 
ACCI 
ACCE 
ACCE 
ACCE 
ACCE 
ACCE 
ACCI 
ACCE 
ACCI 
ACCE 
ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCI 
ACCE 
ACCI 
ACCE 
ACCE 
ACCE 



1. 

l! 
1. 
1. 
1. 
1. 
1. 



1. 
1. 
1. 
1. 
1. 

l! 
l. 

l! 
l. 



l. 
l. 



927 
110 
834 
857 
857 
017 
834 
637 
301 
361 
610 
192 
109 
851 
862 
131 
296 
577 
142 
395 
805 
361 
232 
251 
546 
036 
460 
481 
827 
541 
389 
675 
899 
899 
021 
402 
278 



1. 
1, 

l! 

l. 



l. 
l. 

l! 

l. 



l. 
l. 
l. 
l. 

l! 
1. 

O 

1. 
1. 
1. 
1. 



141 
208 
603 
158 
326 
1.483 
603 
063 
567 
640 
219 
483 
732 
589 
148 
402 
434 
114 
580 
057 
183 
642 
057 
222 
073 
239 
744 
744 
921 
914 
973 
973 
803 
773 
204 
603 
383 



189 



Appendix F 

COMBAT POTENTIAL SCORES MIDEAST AIR WEAPON 

SYSTEMS 



F.l Air Defense Mission 



Glossary 



AWSADX = Air Weapon System Potential - Air Defense 

AFADX = Air Frame Potential - Air Defense 

TAADX = Target Acquisition Potential - Air Defense 

PLADX = Payload Potential - Air Defense 

VADX = Vulnerability to Detection and Engagement - Air Defense 



ACFT 


PRODCC 


ROLE 


AWSADX 


AFADX 


TAADX 


PLADX 


VADX 


FA18L 


US 


FTMR 


2. 


523 


1. 


505 


1. 


262 


2. 


440 


. 672 


F14AC 


US 


FTIN 


2. 


459 


1. 


439 


1. 


674 


2. 


991 


. 820 


F15A 


US 


FTMR 


3. 


746 


1. 


464 


1. 


706 


5. 


264 


. 732 


F15B 


US 


FTTM 


3. 


731 


1. 


441 


1. 


723 


5. 


264 


. 735 


F15C 


US 


FTMR 


4. 


058 


1. 


543 


2. 


007 


5. 


264 


. 711 


F15CFP 


US 


FTMR 


3. 


985 


1. 


510 


2. 


007 


5. 


953 


. 776 


F15D 


US 


FTTM 


4. 


034 


1. 


521 


2. 


007 


5. 


264 


. 714 


F15E 


us 


FTMR 


5. 


242 


1. 


582 


2. 


042 


7. 


762 


. 703 


F16A 


us 


FTMR 


1. 


972 


1. 


502 


, 


916 


1. 


287 


. 606 


F16B 


us 


FTTM 


1. 


972 


1. 


483 


• 


932 


1. 


287 


. 607 


F16C 


us 


FTMR 


2. 


715 


1. 


458 


1. 


452 


2. 


213 


. 622 


F16CSC 


us 


FTMR 


1. 


541 


1. 


463 




916 




991 


. 709 


F16D 


us 


FTTM 


2. 


701 


1. 


437 


1. 


468 


2. 


213 


. 626 


F16J79 


us 


FTMR 


1. 


357 


1. 


252 


■ 


916 


, 


991 


. 761 


F20 


us 


FTMR 


1. 


933 


1. 


349 


1. 


485 


1. 


065 


. 681 


F20A 


us 


FTMR 


2. 


843 


1. 


342 


1. 


485 


2. 


287 


. 596 


F4CD 


us 


FTMR 




978 


1. 


181 


, 


388 


, 


878 


. 779 


F4EF 


us 


FTMR 


1. 


579 


1. 


226 




451 


2. 


259 


. 778 


F4M0D 


us 


FTMR 


2. 


187 


1. 


358 


1. 


279 


2. 


535 


. 773 


F5A 


us 


FTMR 


. 


470 


, 


841 


. 


055 


, 


525 


. 895 


F5B 


us 


FTMR 


, 


473 




835 


, 


071 


. 


525 


. 901 


F5E 


us 


FTMR 


• 


855 


1. 


071 


, 


386 


. 


510 


. 721 


F5F 


us 


FTMR 


• 


800 


1. 


004 


. 


402 


, 


469 


. 739 


F86F 


us 


FTMR 


• 


208 




680 


. 


055 




083 


1. 148 


HARMK80 


UK 


FTMR 


, 


923 


1. 


125 


• 


055 


1. 


170 


. 759 


HAWK200 


UK 


FTMR 


, 


693 


. 


992 


. 


656 


, 


646 


1. 079 


HUNTER 


UK 


FTMR 


. 


250 


. 


783 


. 


055 


. 


103 


1. 097 


HUNTERT 


UK 


FTTM 




256 




787 


. 


071 


, 


103 


1. 101 


KFIRC2 


IS 


FTMR 


1. 


116 


1. 


294 




434 


. 


654 


. 666 


KFIRC7 


IS 


FTMR 


1. 


646 


1. 


390 


1. 


097 


. 


800 


. 661 


KFIRTC2 


IS 


FTTM 




748 


1. 


247 




071 


. 


390 


. 668 


LAV I 


IS 


FTMR 


1. 


402 


1. 


15 6 


1. 


097 


. 


800 


. 729 


LIGHTNG 


UK 


FTIN 


• 


771 


1. 


071 




672 


. 


366 


. 894 


MIG15BIS 


UR 


FTMR 


, 


177 


. 


615 


. 


055 


. 


065 


1. 212 


MIG17F 


UR 


FTMR 


. 


242 


. 


615 


. 


251 


. 


083 


1. 242 


MIG19C 


UR 


FTMR 


• 


424 


. 


798 


. 


247 


. 


269 


. 963 


MIG21C 


UR 


FTIN 


> 


635 


, 


824 


. 


291 


. 


721 


. 904 


MIG21F 


UR 


FTMR 


, 


706 




898 


. 


291 


. 


722 


. 842 


MIG21JKL 


UR 


FTMR 


• 


887 


l! 


016 


, 


412 


• 


868 


. 815 



190 - 



MIG21UM 


UR 


FTTM 


.744 




929 




308 




722 


. 820 


MIG23B 


UR 


FTMR 


1. 103 


1. 


187 




486 


l! 


083 


. 786 


MIG23E 


UR 


FTMR 


. 889 


1. 


192 




412 




868 


. 868 


MIG23G 


UR 


FTMR 


1.258 


1. 


194 




695 


l! 


168 


. 780 


MIG25 


UR 


FTIN 


.879 


1. 


201 




648 


, 


634 


. 908 


MIG25U 


UR 


FTTI 


. 877 


1. 


195 




648 




634 


. 908 


MIG29 


UR 


FTMR 


2. 554 


1. 


416 




854 


2. 


808 


. 633 


MIG31 


UR 


FTIN 


2. 370 


1. 


386 


l! 


624 


2. 


867 


. 820 


MIRF1A 


FR 


FTMR 


.884 


1. 


358 




209 




556 


. 722 


MIRF1B 


FR 


FTTI 


.889 


1. 


346 




225 


. 


556 


. 722 


MIRF1C 


FR 


FTMR 


1.457 


1. 


359 




856 


1. 


029 


. 721 


MIRF1E 


FR 


FTMR 


1. 776 


1. 


531 


l! 


112 


1. 


029 


. 677 


MIRIIIC 


FR 


FTIN 


. 793 


1. 


060 


4 


491 


, 


674 


. 892 


MIRIIIE 


FR 


FTMR 


. 902 


1. 


147 


, 


604 


• 


737 


.884 


MIRIIIEI 


FR 


FTMR 


1.086 


1. 


222 




604 




723 


. 749 


MIR2000C 


FR 


FTMR 


2. 522 


1. 


421 


l! 


387 


1. 


058 


. 636 


MIR2000T 


FR 


FTTM 


2. 515 


1. 


409 


l. 


404 


2. 


058 


. 639 


MIR3NG 


FR 


FTMR 


1.480 


1. 


230 


l. 


112 


1. 


205 


. 794 


MIR4000 


FR 


FTMR 


2. 104 


1. 


609 


l. 


146 


2. 


046 


. 739 


MIR5D1 


FR 


FTIN 


.843 


1. 


160 


, 


435 




737 


. 868 


MIR5D1E 


FR 


FTIN 


1. 624 


1. 


159 


l. 


112 


2. 


032 


.869 


PRCFT6 


CH 


FTTM 


. 365 


• 


803 


■ 


264 


. 


091 


. 990 


PRCF6 


CH 


FTMR 


.413 


• 


801 


, 


247 


• 


273 


. 993 


PRCF7 


CH 


FTIN 


. 626 


• 


836 


■ 


291 


• 


751 


. 936 


PRCF7E 


EG 


FTIN 


. 695 


• 


842 


• 


291 


• 


943 


. 931 


SU27 


UR 


FTMR 


3. 148 


1. 


474 


l. 


796 


3. 


692 


. 729 


TORADV 


UK 


FTIN 


2. 360 


1. 


418 


l. 


566 


2. 


902 


. 822 



191 - 



F.2 Fighter Mission 











Gl 


ossary 












AWSFTR = Ai: 


r Weapon System Potential 


■ Fight 


er 


AFFTR = Air 


Frame 


Potential 


- Fd 


.enter 








TAFTR = Tan 


set Acquisition Potent: 


Lai - 


• F: 


er 


PLFTR = Pay 


load Potential 


- 


Fight* 


»r 








VFTR 


= Vulnerability to Detection 


and 


Engagement 


ACFT 


PRODCC 


ROLE 


AWSFTR 


AFFTR 


TAFTR 


PLFTR 


VFTR 


FA18L 


US 


FTMR 


2. 


185 


1. 


508 


1. 


097 


2. 


026 


. 692 


F14AC 


US 


FTIN 


2. 


045 


1. 


427 


1. 


454 


2. 


426 


. 849 


F15A 


us 


FTMR 


2. 


800 


1. 


423 


1. 


469 


3. 


754 


. 764 


F15B 


us 


FTTM 


2. 


789 


1. 


401 


1. 


495 


3. 


754 


. 768 


F15C 


us 


FTMR 


3. 


065 


1. 


520 


1. 


720 


3. 


754 


. 739 


F15CFP 


us 


FTMR 


3. 


005 


1. 


503 


1. 


720 


4. 


186 


. 795 


F15D 


us 


FTTM 


3. 


041 


1. 


498 


1. 


720 


3. 


754 


. 742 


F15E 


us 


FTMR 


3. 


934 


1. 


576 


1. 


762 


5. 


612 


. 726 


F16A 


us 


FTMR 


2. 


153 


1. 


532 


, 


808 


1. 


726 


. 614 


F16B 


us 


FTTM 


2. 


158 


1. 


513 




834 


1. 


726 


. 614 


F16C 


us 


FTMR 


2. 


392 


1. 


476 


1. 


256 


1. 


877 


. 633 


F16CSC 


us 


FTMR 


1. 


734 


1. 


483 




808 


1. 


354 


. 689 


F16D 


us 


FTTM 


2. 


379 


1. 


454 


l! 


282 


1. 


877 


. 638 


F16J79 


us 


FTMR 


1. 


525 


1. 


276 


■ 


808 


1. 


354 


. 739 


F20 


us 


FTMR 


2. 


125 


1. 


393 


l. 


284 


1. 


478 


. 649 


F20A 


us 


FTMR 


2. 


576 


1. 


382 


i. 


284 


2. 


001 


. 594 


F4CD 


us 


FTMR 


« 


968 


1. 


147 


, 


379 


■ 


875 


. 807 


F4EF 


us 


FTMR 


1. 


420 


1. 


220 


, 


431 


1. 


954 


.809 


F4MOD 


us 


FTMR 


1. 


880 


1. 


350 


l. 


124 


2. 


156 


. 802 


F5A 


us 


FTMR 


• 


579 


, 


861 


, 


, 088 


• 


720 


. 920 


F5B 


us 


FTMR 


■ 


584 


■ 


856 


, 


, 114 


■ 


720 


. 926 


F5E 


us 


FTMR 


• 


993 


1. 


099 




364 


• 


701 


. 713 


F5F 


us 


FTMR 


• 


924 


1. 


035 


, 


, 391 


, 


632 


. 731 


F86F 


us 


FTMR 


• 


258 


. 


673 


, 


088 




141 


1. 149 


HARMK80 


UK 


FTMR 


1. 


242 


1. 


221 


, 


088 


1. 


528 


. 720 


HAWK200 


UK 


FTMR 


• 


859 


1. 


055 


, 


590 


• 


868 


. 962 


HUNTER 


UK 


FTMR 


■ 


326 


• 


804 


■ 


088 




175 


1. 076 


HUNTERT 


UK 


FTTM 


• 


334 


• 


806 


, 


114 


, 


175 


1. 081 


KFIRC2 


IS 


FTMR 


1. 


198 


1. 


242 


, 


405 




878 


. 687 


KFIRC7 


IS 


FTMR 


1. 


657 


1. 


370 


• 


959 


1. 


070 


. 681 


KFIRTC2 


IS 


FTTM 


• 


871 


1. 


197 


, 


114 




546 


. 690 


LAV I 


IS 


FTMR 


1. 


516 


1. 


230 


, 


959 


L 


070 


. 714 


LIGHTNG 


UK 


FTIN 


• 


781 


1. 


039 


, 


604 


. 


509 


. 920 


MIG15BIS 


UR 


FTMR 


• 


238 


• 


644 


■ 


088 


• 


111 


1. 170 


MIG17F 


UR 


FTMR 


• 


294 


• 


652 


, 


252 


. 


140 


1. 193 


MIG19C 


UR 


FTMR 


• 


564 


. 


844 


, 


249 


. 


376 


. 858 


MIG21C 


UR 


FTIN 


, 


757 


. 


854 


, 


286 


. 


748 


. 808 


MIG21F 


UR 


FTMR 




834 




914 


, 


286 


, 


750 


. 758 


MIG21JKL 


UR 


FTMR 


1. 


038 


1. 


020 


, 


387 


, 


946 


. 736 


MIG21UM 


UR 


FTTM 




876 




937 




312 


. 


750 


. 742 


MIG23B 


UR 


FTMR 


1. 


054 


l! 


141 


, 


448 


. 


912 


. 774 


MIG23E 


UR 


FTMR 


, 


979 


l. 


150 


, 


387 




946 


. 824 


MIG23G 


UR 


FTMR 


1. 


192 


l. 


148 




623 


1. 


020 


. 768 


MIG25 


UR 


FTIN 


. 


796 


l. 


124 




583 


, 


494 


. 923 


MIG25U 


UR 


FTTI 




794 


l. 


118 




583 




494 


. 923 


MIG29 


UR 


FTMR 


2. 


057 


l. 


436 




756 


l! 


968 


. 653 


MIG31 


UR 


FTIN 


1. 


803 


l. 


308 


l. 


412 


2. 


067 


. 873 


MIRF1A 


FR 


FTMR 


1. 


069 


l. 


330 


. 


217 


. 


753 


. 697 


MIRF1B 


FR 


FTTI 


1. 


078 


l. 


319 




243 




753 


. 697 


MIRF1C 


FR 


FTMR 


1. 


512 


l. 


331 


, 


758 


1. 


105 


. 695 


MIRF1E 


FR 


FTMR 


1. 


760 


l. 


457 


. 


972 


1. 


105 


. 666 


MIRIIIC 


FR 


FTIN 




895 


i. 


037 


. 


453 


. 


762 


. 825 


MIRIIIE 


FR 


FTMR 


1. 


004 


l. 


115 


, 


547 


. 


841 


. 820 



- Fighter 



- 192 



MIRIIIEI 


FR 


FTMR 


1. 105 


1. 185 




547 




823 


. 760 


MIR2000C 


FR 


FTMR 


2. 130 


1.414 


l! 


202 


1. 


631 


. 657 


MIR2000T 


FR 


FTTM 


2. 127 


1.401 


l. 


228 


1. 


631 


. 661 


MIR3NG 


FR 


FTMR 


1. 531 


1. 228 




972 


1. 


322 


. 759 


MIR4000 


FR 


FTMR 


1. 806 


1. 621 


l! 


000 


1. 


611 


. 768 


MIR5D1 


FR 


FTIN 


. 955 


1. 120 




406 




841 


. 808 


MIR5D1E 


FR 


FTIN 


1.489 


1. 120 


, 


972 


l! 


586 


. 809 


PRCFT6 


CH 


FTTM 


.422 


. 810 


, 


275 




154 


. 984 


PRCF6 


CH 


FTMR 


.484 


. 814 


, 


249 




382 


. 983 


PRCF7 


CH 


FTIN 


. 761 


. 871 


• 


286 


, 


799 


. 832 


PRCF7E 


EG 


FTIN 


.857 


.881 


, 


286 


1. 


040 


. 826 


SU27 


UR 


FTMR 


2. 260 


1.460 


l. 


543 


2. 


194 


. 757 


TORADV 


UK 


FTIN 


2. 130 


1.403 


l. 


364 


2. 


501 


.806 



193 



F.3 Interdiction Mission 



Glossary 



AWSINT = Air Weapon System Potential - Interdiction 

AFINT = Air Frame Potential - Interdiction 

TAINT = Target Acquisition Potential - Interdiction 

PLINT = Payload Potential - Interdiction 

VINT = Vulnerability to Detection and Engagement - Interdiction 



ACFT 


PRODCC 


ROLE 


AWSINT 


AFINT 


TAINT 


PLINT 


VINT 


ALPHAMS2 


FR 


FTAT 


• 


538 


• 


784 


.222 


• 


789 


1.074 


AMX 


IT 


FTAT 


• 


895 


. 


922 


.430 


1. 


208 


. 942 


A10A 


US 


FTAT 


• 


670 


• 


737 


. 190 


2. 


047 


1. 501 


A37B 


US 


FTAT 


• 


282 


- 


501 


. 139 


, 


603 


1.434 


A4H 


US 


FTAT 




753 


• 


922 


. 274 


1. 


172 


1.025 


A4KU 


US 


FTAT 




565 


• 


702 


.274 




961 


1. 126 


A4N 


US 


FTAT 


• 


855 


• 


800 


. 274 


1. 


470 


. 990 


A7E 


US 


FTAT 


1. 


061 


1. 


012 


. 190 


1. 


908 


. 971 


A7P 


US 


FTAT 




730 


• 


881 


. 107 


1. 


569 


1. 155 


BAC167 


UK 


FTAT 


, 


204 


• 


503 


. 139 


, 


390 


1. 605 


C101BB 


SP 


FTAT 


, 


270 


. 


575 


. 139 


, 


634 


1. 613 


C101CC 


SP 


FTAT 


• 


288 


• 


609 


. 139 




634 


1.541 


C101DD 


SP 


FTTA 


• 


396 


• 


609 


. 139 


1. 


095 


1.541 


FA18L 


US 


FTMR 


2. 


272 


1. 


374 


. 882 


2. 


066 


. 634 


F104GCF 


US 


FTAT 


. 


785 


1. 


014 


. 107 


• 


948 


.834 


F15A 


US 


FTMR 


1. 


848 


1. 


331 


1.055 


1. 


480 


. 694 


F15B 


US 


FTTM 


1. 


847 


1. 


306 


1. 087 


1. 


480 


. 697 


F15C 


US 


FTMR 


2. 


024 


1. 


414 


1.227 


1. 


480 


. 676 


F15CFP 


us 


FTMR 


1. 


951 


1. 


388 


1. 227 


1. 


694 


. 737 


F15D 


us 


FTTM 


2. 


008 


1. 


395 


1. 227 


1. 


480 


. 679 


F15E 


us 


FTMR 


2. 


760 


1. 


438 


1. 379 


2. 


637 


. 669 


F16A 


us 


FTMR 


2. 


248 


1. 


366 


. 683 


1. 


837 


. 571 


F16B 


us 


FTTM 


2. 


261 


1. 


352 


. 716 


1. 


837 


. 571 


F16C 


us 


FTMR 


2. 


374 


1. 


343 


. 991 


1. 


837 


. 586 


F16CSC 


us 


FTMR 


1. 


667 


1. 


347 


. 601 


1. 


496 


. 675 


F16D 


us 


FTTM 


2. 


374 


1. 


329 


1.023 


1. 


837 


. 589 


F16J79 


us 


FTMR 


1. 


415 


1. 


126 


. 601 


1. 


379 


. 723 


F20 


us 


FTMR 


1. 


790 


1. 


245 


. 928 


1. 


327 


. 646 


F20A 


us 


FTMR 


2. 


068 


1. 


238 


. 928 


1. 


327 


. 559 


F4CD 


us 


FTMR 


1. 


078 


1. 


087 


. 321 


1. 


056 


. 735 


F4EF 


us 


FTMR 


1. 


536 


1. 


110 


.466 


1. 


822 


. 734 


F4M0D 


us 


FTMR 


2. 


150 


1. 


195 


. 941 


2. 


498 


. 730 


F5A 


us 


FTMR 


, 


526 


. 


754 


. 107 


, 


628 


. 893 


F5B 


us 


FTMR 


, 


534 


a 


749 


. 139 


. 


628 


. 899 


F5E 


us 


FTMR 


. 


990 


. 


968 


.297 


, 


820 


. 673 


F5F 


us 


FTMR 


• 


994 


, 


906 


.438 


. 


776 


. 690 


F86F 


us 


FTMR 


■ 


263 


. 


613 


. 107 


. 


258 


1. 132 


G91Y 


IT 


FTAT 


■ 


469 




722 


.244 




644 


1. 103 


HARMK80 


UK 


FTMR 


1. 


070 


1. 


020 


.216 


1. 


131 


. 713 


HAWK200 


UK 


FTMR 


, 


808 


, 


874 


. 534 


1. 


072 


1. 014 


HAWK50T 


UK 


FTTA 


. 


312 


. 


670 


. 139 




360 


1. 161 


HAWK60A 


UK 


FTAT 


, 


446 


. 


762 


. 190 


. 


655 


1. 148 


HAWK60T 


UK 


FTTA 




381 


. 


773 


. 107 


. 


510 


1. 135 


HUNTER 


UK 


FTMR 




380 




694 


. 107 


, 


518 


1. 089 


HUNTERT 


UK 


FTTM 


. 


389 




693 


. 139 




518 


1. 093 


IL28 


UR 


BMAT 




236 




578 


. 139 




637 


1. 854 


JAGI04 


UK 


FTAT 


1. 


001 


1. 


126 


. 190 


1. 


118 


. 776 


JAGI11 


UK 


FTAT 


1. 


020 


1. 


142 


. 190 


1. 


118 


. 765 


JASTREB 


YU 


FTAT 


, 


230 


. 


463 


. 107 




544 


1. 567 


KFIRC2 


IS 


FTMR 


1. 


519 


1. 


199 


. 325 


l! 


400 


. 624 


KFIRC7 


IS 


FTMR 


1. 


898 


1. 


262 


. 705 


l. 


593 


. 619 


KFIRTC2 


IS 


FTTM 


1. 


387 


1. 


158 


. 139 


l. 


400 


. 626 


LAV I 


IS 


FTMR 


1. 


662 


1. 


015 


. 705 


i. 


679 


. 686 



194 



L29 


CZ 


FTTA 


.098 


i 


360 


. 139 




133 


1. 998 


MB326K 


IT 


FTAT 


. 244 




438 


. 107 




568 


1.482 


MB326L 


IT 


FTTA 


. 226 




438 


. 139 


i 


459 


1.482 


MB339C 


IT 


FTAT 


.443 


« 


610 


. 190 




908 


1. 268 


MB339K 


IT 


FTAT 


. 325 




532 


. 107 




635 


1. 268 


MIG15BIS 


UR 


FTMR 


. 229 




549 


. 107 




239 


1. 195 


MIG17F 


UR 


FTMR 


. 261 


i 


551 


.219 




249 


1. 225 


MIG19C 


UR 


FTMR 


.408 




706 


. 218 


■ 


283 


. 915 


MIG21F 


UR 


FTMR 


. 539 


i 


797 


.243 




350 


. 800 


MIG21JKL 


UR 


FTMR 


. 643 




878 


. 312 




403 


. 774 


MIG21UM 


UR 


FTTM 


. 579 


, 


825 


. 275 




350 


. 779 


MIG23B 


UR 


FTMR 


. 878 


1. 


075 


. 354 




647 


. 745 


MIG23E 


UR 


FTMR 


. 749 


1. 


079 


. 312 




597 


.830 


MIG23F 


UR 


FTAT 


. 566 


g 


918 


. 190 


i 


597 


. 941 


MIG23G 


UR 


FTMR 


. 947 


1. 


081 


.475 


■ 


647 


. 740 


MIG27DJ 


UR 


FTAT 


. 760 


, 


894 


. 190 




885 


.829 


MIG29 


UR 


FTMR 


1. 759 


1. 


300 


. 648 


l! 


284 


. 599 


MIRF1A 


FR 


FTMR 


1. 077 


1. 


174 


.278 




866 


. 679 


MIRF1C 


FR 


FTMR 


1. 287 


1. 


188 


. 649 


, 


866 


. 678 


MIRF1E 


FR 


FTMR 


1. 521 


1. 


342 


. 796 




866 


. 637 


MIRIIIE 


FR 


FTMR 


. 903 


, 


992 


.422 


, 


926 


.839 


MIRIIIEI 


FR 


FTMR 


1. 346 


1. 


051 


.422 


1. 


399 


. 701 


MIR2000C 


FR 


FTMR 


2. 190 


1. 


300 


. 981 


1. 


660 


. 599 


MIR2000T 


FR 


FTTM 


1. 999 


1. 


290 


1.013 


1. 


333 


. 602 


MIR3NG 


FR 


FTMR 


1. 357 


1. 


134 


. 714 


1. 


231 


. 747 


MIR4000 


FR 


FTMR 


2.026 


1. 


360 


.842 


2. 


069 


. 703 


MIR5DD 


FR 


FTTA 


. 750 


1. 


067 


.228 


, 


716 


.840 


MIR5D2 


FR 


FTAT 


. 903 


1. 


103 


. 325 


, 


942 


. 839 


PRCA5 


CH 


FTAT 


.492 


■ 


726 


. 107 


• 


654 


. 958 


PRCFT6 


CH 


FTTM 


. 323 


■ 


709 


. 250 


, 


098 


. 987 


PRCF6 


CH 


FTMR 


. 312 


, 


715 


. 218 


, 


098 


. 990 


SUPETEN 


FR 


FTAT 


. 745 


■ 


856 


. 325 




882 


.898 


SU20 


UR 


FTAT 


. 992 


• 


999 


. 202 


l! 


127 


. 756 


SU22 


UR 


FTAT 


1.216 


1. 


031 


. 311 


l. 


478 


. 761 


SU25 


UR 


FTAT 


.493 


■ 


596 


. 190 


l. 


425 


1.510 


SU27 


UR 


FTMR 


1. 831 


1. 


205 


1. 106 


l. 


487 


. 693 


SU7BMKL 


UR 


FTAT 


.425 


• 


626 


.202 


■ 


452 


. 955 


SU7U 


UR 


FTTA 


. 399 


. 


615 


. 139 


, 


452 


. 952 


TA4EH 


US 


FTTA 


. 602 


• 


766 


. 306 


■ 


971 


1. 112 


TA4KU 


US 


FTTA 


. 561 


. 


671 


. 306 


. 


937 


1. 126 


TORIDS 


UK 


FTAT 


1. 897 


1. 


291 


. 874 


2. 


160 


. 764 


TU16AG 


UR 


BMAT 


.441 


• 


781 


. 353 


1. 


354 


1. 878 


TU22BD 


UR 


BMAT 


. 637 


• 


933 


. 353 


1. 


728 


1. 577 



195 



F.4 Close Air Support Mission (CAS) 











Gl 


ossary 










AWSCAS = Ai: 


r Weapon System Potential 


• CAS 




AFCAS = Air 


Frame 


Potential 


- CAS 








TACAS = Tar; 


?et Acquisition Potential - 


■ CA 




PLCAS = Pay 


load Potential 


- 


CAS 










VCAS 


= Vulnerability to Detection and 


Engagement 


ACFT 


PRODCC 


ROLE 


AWSCAS 


AFCAS 


TACAS 


PLCAS 


VCAS 


ALPHAMS1 


FR 


FTTC 


• 


531 


• 


827 


.204 




432 


. 868 


ALPHAMS2 


FR 


FTAT 


• 


805 


• 


900 


. 299 




776 


.804 


AMX 


IT 


FTAT 


1. 


250 


1. 


033 


. 341 


1. 


268 


. 711 


A10A 


US 


FTAT 


1. 


302 




910 


.252 


2. 


661 


1.052 


A37B 


US 


FTAT 


• 


484 


• 


574 


. 204 




661 


. 997 


A4H 


US 


FTAT 


1. 


037 


1. 


077 


.219 


1. 


139 


. 780 


A4KU 


US 


FTAT 


. 


810 


. 


788 


. 219 


1. 


031 


. 851 


A4N 


US 


FTAT 


1. 


121 


_ 


908 


.219 


1. 


361 


. 758 


A7E 


US 


FTAT 


1. 


743 


1. 


160 


. 252 


2. 


351 


. 755 


A7P 


US 


FTAT 


1. 


325 


1. 


016 


. 157 


2. 


141 


. 877 


BAC167 


UK 


FTAT 


. 


349 


• 


584 


.204 


. 


446 


1. 149 


CM170 


FR 


FTTC 


• 


314 


, 


553 


.204 


• 


419 


1.215 


CM170I 


IS 


FTTC 


• 


314 


, 


553 


.204 


• 


419 


1. 215 


C101BB 


SP 


FTAT 


• 


473 


♦ 


693 


.204 


• 


712 


1. 129 


C101CC 


SP 


FTAT 


• 


501 


• 


733 


.204 




712 


1.087 


C101DD 


SP 


FTTA 


• 


608 




733 


.204 


1. 


009 


1.087 


FA18L 


US 


FTMR 


2. 


593 


1. 


445 


.509 


2. 


046 


.525 


F104GCF 


US 


FTAT 


1. 


279 


, 


977 


. 157 


1. 


442 


. 688 


F15A 


US 


FTMR 


2. 


247 


1. 


367 


. 509 


1. 


998 


. 588 


F15B 


US 


FTTM 


2. 


247 


1. 


333 


. 556 


1. 


998 


. 591 


F15C 


us 


FTMR 


2. 


410 


1. 


482 


. 573 


1. 


998 


. 570 


F15CFP 


us 


FTMR 


2. 


362 


1. 


518 


. 573 


2. 


146 


. 610 


F15D 


us 


FTTM 


2. 


387 


1. 


456 


. 573 


1. 


998 


. 573 


F15E 


us 


FTMR 


3. 


115 


1. 


529 


. 749 


2. 


764 


. 560 


F16A 


us 


FTMR 


2. 


721 


1. 


441 


.435 


1. 


842 


.461 


F16B 


us 


FTTM 


2. 


743 


1. 


423 


.482 


1. 


842 


.462 


F16C 


us 


FTMR 


2. 


699 


1. 


408 


.549 


1. 


842 


.476 


F16CSC 


us 


FTMR 


2. 


103 


1. 


414 


. 340 


1. 


632 


. 539 


F16D 


us 


FTTM 


2. 


702 


1. 


388 


. 596 


1. 


842 


.480 


F16J79 


us 


FTMR 


1. 


802 


1. 


161 


. 340 


1. 


551 


. 574 


F20 


us 


FTMR 


2. 


329 


1. 


310 


.462 


1. 


691 


. 502 


F20A 


us 


FTMR 


2. 


651 


1. 


300 


.462 


1. 


691 


.440 


F4CD 


us 


FTMR 


1. 


094 


1. 


091 


. 271 




731 


. 614 


F4EF 


us 


FTMR 


1. 


944 


1. 


120 


.410 


1. 


936 


. 616 


F4M0D 


us 


FTMR 


2. 


401 


1. 


235 


. 587 


2. 


401 


. 612 


F5A 


us 


FTMR 


, 


832 


, 


760 


. 157 


. 


773 


. 673 


F5B 


us 


FTMR 




849 




756 


.204 


. 


773 


. 677 


F5E 


us 


FTMR 


1. 


342 


1. 


015 


.227 


. 


898 


. 523 


F5F 


us 


FTMR 


1. 


288 


. 


940 


.400 


. 


769 


. 535 


F86F 


us 


FTMR 


t 


397 


, 


592 


. 157 


. 


381 


. 911 


G91Y 


IT 


FTAT 




655 




786 


. 208 




691 


. 844 


HARMK80 


UK 


FTMR 


1. 


578 


1. 


157 


.282 


L. 


080 


. 526 


HAWK200 


UK 


FTMR 


1. 


065 


1. 


029 


. 380 


1. 


032 


. 760 


HAWK50T 


UK 


FTTA 


, 


513 


, 


738 


. 204 


. 


461 


. 875 


HAWK60A 


UK 


FTAT 




664 


. 


859 


. 252 


. 


671 


. 873 


HAWK60T 


UK 


FTTA 


. 


595 


, 


875 


. 157 


. 


568 


. 859 


HUNTER 


UK 


FTMR 


. 


567 


. 


718 


. 157 


. 


614 


. 859 


HUNTERT 


UK 


FTTM 




582 




717 


. 204 




614 


. 863 


JAGI04 


UK 


FTAT 


1. 


405 


1. 


216 


. 252 


1. 


120 


. 605 


JAGI11 


UK 


FTAT 


1. 


433 


1. 


234 


. 252 


1. 


120 


. 597 


JASTREB 


YU 


FTAT 




405 




533 


. 157 




652 


1. 114 


KFIRC2 


IS 


FTMR 


l! 


740 


l! 


199 


. 238 


1. 


256 


. 514 


KFIRC7 


IS 


FTMR 


2. 


035 


l. 


292 


. 379 


1. 


432 


. 509 



- CAS 



196 



KFIRTC2 


IS 


FTTM 


1. 683 


1. 


146 


. 204 


1. 


256 


. 516 


LAV I 


IS 


FTMR 


1. 901 


1. 


099 


. 379 


1. 


491 


. 530 


L29 


CZ 


FTTA 


. 162 




427 


. 204 




094 


1. 369 


L39ZA 


CZ 


FTTC 


. 310 




511 


. 204 




457 


1. 243 


MB326K 


IT 


FTAT 


. 395 


■ 


474 


. 157 




643 


1. 094 


MB326L 


IT 


FTTA 


.296 


■ 


474 


.204 




324 


1. 094 


MB339A 


IT 


FTTC 


. 385 




546 


. 204 




349 


. 916 


MB339C 


IT 


FTAT 


. 690 




690 


.252 




928 


. 919 


MB339K 


IT 


FTAT 


. 549 




586 


. 157 


i 


751 


. 919 


MIG15BIS 


UR 


FTMR 


. 362 


■ 


565 


. 157 




325 


. 919 


MIG15UTI 


UR 


FTTC 


. 321 


• 


528 


. 204 




218 


. 924 


MIG17F 


UR 


FTMR 


. 395 


• 


579 


. 198 


■ 


374 


. 936 


MIG19C 


UR 


FTMR 


.590 


• 


729 


. 198 




409 


. 717 


MIG21F 


UR 


FTMR 


. 673 


• 


778 


.207 


i 


395 


. 646 


MIG21JKL 


UR 


FTMR 


.806 


, 


862 


. 233 


• 


503 


. 630 


MIG21UM 


UR 


FTTM 


. 717 




795 


. 254 


4 


395 


. 634 


MIG23B 


UR 


FTMR 


. 953 


1. 


034 


. 249 




669 


. 655 


MIG23E 


UR 


FTMR 


.851 


1. 


042 


.233 


a 


632 


. 714 


MIG23F 


UR 


FTAT 


. 715 


, 


894 


. 252 




632 


. 802 


MIG23G 


UR 


FTMR 


. 986 


1. 


040 


. 293 


I 


669 


. 651 


MIG23UM 


UR 


FTTC 


.825 


* 


948 


. 280 




669 


. 742 


MIG27DJ 


UR 


FTAT 


1.034 


. 


897 


. 252 




968 


. 682 


MIG29 


UR 


FTMR 


1. 916 


1. 


316 


.422 


l! 


164 


.497 


MIRF1A 


FR 


FTMR 


1. 314 


1. 


188 


.284 




898 


. 585 


MIRF1C 


FR 


FTMR 


1.405 


1. 


207 


.422 


, 


898 


. 584 


MIRF1E 


FR 


FTMR 


1.536 


1. 


313 


.477 


• 


898 


. 564 


MIRIIIE 


FR 


FTMR 


1.035 


1. 


006 


. 274 




912 


. 696 


MIRIIIEI 


FR 


FTMR 


1. 526 


1. 


084 


.274 


l! 


219 


. 564 


MIR2000C 


FR 


FTMR 


2.251 


1. 


316 


. 566 


l. 


461 


.497 


MIR2000T 


FR 


FTTM 


2. 105 


1. 


302 


. 613 


l. 


259 


.499 


MIR3NG 


FR 


FTMR 


1.420 


1. 


204 


. 382 


l. 


129 


. 630 


MIR4000 


FR 


FTMR 


2. 068 


1. 


430 


. 515 


l. 


709 


. 594 


MIR5DD 


FR 


FTTA 


. 959 


1. 


084 


. 237 


, 


775 


. 705 


MIR5D2 


FR 


FTAT 


1.061 


1. 


132 


. 238 




924 


. 704 


OV10D 


US 


MIAT 


.596 


• 


524 


. 329 


l! 


517 


1.419 


PRCA5 


CH 


FTAT 


. 728 


• 


722 


. 157 


■ 


815 


. 778 


PRCFT6 


CH 


FTTM 


.482 


, 


690 


.245 


, 


287 


. 791 


PRCF6 


CH 


FTMR 


.468 


■ 


706 


. 198 


■ 


287 


. 790 


SF260MW 


IT 


MIT A 


. 122 


• 


542 


. 204 




184 


2. 349 


SF260TP 


IT 


MITA 


. 164 


• 


466 


.204 


■ 


184 


1. 627 


SUPETEN 


FR 


FTAT 


. 935 


■ 


918 


. 238 


, 


903 


. 728 


SU20 


UR 


FTAT 


1. 103 


. 


978 


. 192 


. 


951 


. 635 


SU22 


UR 


FTAT 


1. 319 


1. 


020 


. 317 


l. 


179 


. 639 


SU25 


UR 


FTAT 


. 744 


• 


721 


. 252 


l. 


284 


1. 050 


SU27 


UR 


FTMR 


1. 734 


1. 


213 


. 528 


l. 


303 


. 585 


SU7BMKL 


UR 


FTAT 


. 596 


. 


634 


. 192 


, 


500 


. 724 


SU7U 


UR 


FTTA 


.597 


, 


618 


. 204 




500 


. 722 


TA4EH 


US 


FTTA 


.850 


. 


868 


. 266 


l! 


009 


.844 


TA4KU 


US 


FTTA 


. 799 


. 


747 


. 266 


, 


994 


. 851 


TORIDS 


UK 


FTAT 


1. 935 


1. 


372 


. 562 


l. 


746 


. 642 



197- 



Appendix G 
MIDDLE EASTERN AIR COMBAT POTENTIAL 1984-1990 



NOTE: Depicted in Air Combat Potential Units undepreciated for 
maintenance force quality. 



YEAR 


INVENTOR 


Y AIR 


FIGHTER 


INTERDICTI 


CAS 






DEFENSE 












Algeria 


















1984 


294 


59. 


28 


46. 


89 


17. 


87 


68. 70 


1985 


295 


58. 


12 


46. 


18 


18. 


43 


68. 12 


1986 


285 


58. 


12 


46. 


18 


17. 


47 


64. 83 


1987 


275 


58. 


12 


46. 


18 


16. 


80 


62. 97 


1988 


266 


69. 


17 


50. 


88 


15. 


85 


59. 68 


1989 


259 


86. 


69 


58. 


17 


14. 


90 


56. 38 


1990 


239 


97. 


25 


62. 


47 


12. 


99 


49. 80 


Bahrain 


















1984 


6 


• 


97 




77 




58 


1. 86 


1985 


8 


1. 


29 


l! 


03 


• 


77 


2.49 


1986 


12 


1. 


93 


i. 


53 


1. 


16 


3. 72 


1987 


12 


1. 


93 


l. 


53 


1. 


16 


3. 72 


1988 


12 


1. 


93 


l. 


53 


1. 


16 


3. 72 


1989 


12 


1. 


93 


l. 


53 


1. 


16 


3. 72 


1990 


12 


1. 


93 


l. 


53 


1. 


16 


3. 72 


Egypt 


















1984 


441 


165. 


00 


130. 


90 


31. 


66 


98. 93 


1985 


450 


158. 


57 


125. 


61 


35. 


66 


111. 72 


1986 


441 


166. 


56 


129. 


34 


34. 


16 


107. 05 


1987 


437 


189. 


81 


140. 


44 


31. 


15 


97. 86 


1988 


419 


202. 


51 


145. 


21 


36. 


58 


107. 27 


1989 


399 


194. 


81 


138. 


86 


43. 


00 


120. 75 


1990 


399 • 


194. 


81 


138. 


86 


43. 


00 


120. 75 


Ethiopia 


















1984 


138 




00 




00 


12. 


31 


37. 72 


1985 


153 


, 


00 


. 


00 


13. 


01 


39. 94 


1986 


148 


■ 


00 


, 


00 


12. 


06 


36. 73 


1987 


148 


. 


00 




00 


12. 


06 


36. 73 


1988 


150 


. 


00 




00 


12. 


59 


38. 62 


1989 


150 


• 


00 




00 


12. 


49 


38. 95 


1990 


150 


• 


00 


• 


00 


12. 


40 


39. 29 


Iran 


















1984 


110 


97. 


55 


59. 


59 


62. 


16 


156. 85 


1985 


94 


73. 


23 


44. 


52 


49. 


98 


127. 16 


1986 


76 


58. 


68 


35. 


72 


39. 


97 


102. 35 


1987 


56 


40. 


85 


24. 


94 


28. 


43 


73. 08 


1988 


47 


25. 


55 


15. 


63 


25. 


80 


66. 35 


1989 


47 


25. 


55 


15. 


63 


25. 


80 


66. 35 


1990 


47 


25. 


55 


15. 


63 


25. 


80 


66. 35 



198 



YEAR 


INVENTORY 


AIR 
DEFENSE 


Iraq 






1984 


457 


222. 64 


1985 


508 


256.49 


1986 


541 


240. 82 


1987 


563 


249. 18 


1988 


556 


247. 39 


1989 


556 


261. 72 


1990 


556 


276. 05 


Israel 






1984 


491 


427. 70 


1985 


509 


452.25 


1986 


527 


534. 31 


1987 


541 


612. 34 


1988 


544 


658. 01 


1989 


523 


669. 14 


1990 


499 


646.84 


Jordan 






1984 


125 


41. 81 


1985 


126 


46. 34 


1986 


126 


46. 34 


1987 


132 


46. 34 


1988 


130 


46. 34 


1989 


134 


46. 34 


1990 


134 


46. 34 


Kuwait 






1984 


64 


7.22 


1985 


80 


12. 87 


1986 


89 


16. 37 


1987 


89 


16. 37 


1988 


89 


16. 37 


1989 


89 


16. 37 


1990 


89 


16. 37 


Lebanon 






1984 


5 


.00 


1985 


3 


.00 


1986 





.00 


1987 ■ 





.00 


1988 





.00 


1989 





.00 


1990 





. 00 



FIGHTER INTERDICTION 



CAS 



182. 


70 


209. 


77 


195. 


07 


198. 


98 


190. 


79 


196. 


01 


201. 


23 


280. 


31 


297. 


86 


346. 


97 


397. 


53 


428. 


10 


434. 


92 


419. 


70 


29. 


28 


32. 


53 


32. 


53 


32. 


53 


32. 


53 


32. 


53 


32. 


53 


5. 


15 


9. 


09 


11. 


55 


11. 


55 


11. 


55 


11. 


55 


11. 


55 


• 


00 


. 


00 


. 


00 


• 


00 


• 


00 




00 


# 


00 



32. 
40. 
55. 
61. 
64, 
64. 
64. 



47, 
46. 
46. 
46. 
43. 
44. 
44. 



3. 
2. 
2. 
2. 
2. 
2. 
2. 



33 
73 
96 
71 
85 
85 
85 



257. 87 

272.47 

289. 

316. 

331. 

328. 

316. 



89 
77 
95 
01 
10 



87 
58 
58 
58 
73 
45 
45 



39 
98 
98 
98 
98 
98 
98 



. 00 
. 00 
. 00 
. 00 
. 00 
. 00 
. 00 



146. 
141. 
141. 
152 = 
152. 
158. 
158. 



199 



YEAR 


INVENTORY 


AIR 


FIGHTER 


INTERDICTI 


CAS 






DEFENSE 












Libya 


















1984 


460 


17. 


70 


12. 


51 


8. 


82 


29. 65 


1985 


505 


19. 


66 


13. 


91 


9. 


11 


30. 60 


1986 


528 


21. 


75 


15. 


18 


9. 


11 


30. 60 


1987 


527 


22. 


22 


15. 


29 


9. 


11 


30. 60 


1988 


530 


25. 


86 


17. 


22 


8. 


99 


30. 49 


1989 


530 


28. 


95 


18. 


64 


8. 


99 


30. 49 


1990 


518 


28. 


83 


18. 


31 


8. 


99 


30. 49 


Morocco 


















1984 


94 


• 


00 


• 


00 


34. 


85 


115. 83 


1985 


93 


* 


00 


• 


00 


34. 


25 


114.47 


1986 


93 


a 


00 


• 


00 


34. 


25 


114.47 


1987 


93 


• 


00 


■ 


00 


34. 


25 


114.47 


1988 


93 


■ 


00 


• 


00 


34. 


25 


114.47 


1989 


93 


■ 


00 


• 


00 


34. 


25 


114.47 


1990 


93 


• 


00 


i 


00 


34. 


25 


114.47 


Oman 


















1984 


52 


• 


00 


• 


00 


9. 


60 


41. 91 


1985 


52 


, 


00 


« 


00 


9. 


60 


41. 91 


1986 


50 




00 


, 


00 


9. 


36 


41. 06 


1987 


46 


7. 


13 


4. 


45 


8. 


48 


37. 89 


1988 


50 


14. 


26 


8. 


90 


8. 


48 


37. 89 


1989 


50 


14. 


26 


8. 


90 


8. 


48 


37. 89 


1990 


50 


14. 


26 


8. 


90 


8. 


48 


37.89 


Qatar 


















1984 


15 


e 


00 




00 


1. 


14 


3. 75 


1985 


21 


, 


00 


• 


00 


1. 


84 


5. 82 


1986 


24 


• 


00 


■ 


00 


2. 


04 


6. 35 


1987 


22 


• 


00 


e 


00 


1. 


99 


6. 14 


1988 


22 


, 


00 


, 


00 


1. 


99 


6. 14 


1989 


22 


, 


00 


, 


00 


1. 


99 


6. 14 


1990 


22 


• 


00 


• 


00 


1. 


99 


6. 14 


Saudi Arab 


ia 
















1984 


183 


183. 


85 


97. 


20 


52. 


11 


156. 09 


1985 


192 


209. 


79 


110. 


72 


52. 


11 


156. 09 


1986 


198 


212. 


17 


111. 


87 


55. 


27 


165. 45 


1987 


202 


204. 


45 


106. 


49 


68. 


98 


198. 08 


1988 


214 


226. 


56 


120. 


31 


71. 


53 


199. 05 


1989 


220 


248. 


66 


134. 


13 


70. 


29 


190. 36 


1990 


220 


248. 


66 


134. 


13 


70. 


29 


190. 36 



200 



YEAR 


INVENTORY 


AIR 


FIGHT] 


INTERDIC 


CAS 






DEFENSE 












Somalia 


















1984 


64 


3. 


45 


2. 


79 


2. 


20 


8.35 


1985 


64 


3. 


45 


2. 


79 


2. 


20 


8. 35 


1986 


64 


3. 


45 


2. 


79 


2. 


20 


8. 35 


1987 


64 


3. 


45 


2. 


79 


2. 


20 


8. 25 


1988 


64 


3. 


45 


2. 


79 


2, 


20 


8. 35 


1989 


64 


3. 


45 


2. 


79 


2. 


20 


8. 35 


1990 


64 


3. 


45 


2. 


79 


2. 


20 


8. 35 


Sudan 


















1984 


35 


4. 


60 


3. 


84 


3. 


74 


12.51 


1985 


35 


4. 


60 


3. 


84 


3. 


74 


12.51 


1986 


46 


5. 


86 


4. 


88 


4. 


90 


18.13 


1987 


52 


7. 


11 


5. 


91 


5. 


61 


21.83 


1988 


49 


7. 


11 


5. 


91 


5. 


29 


20.8. 


1989 


46 


7. 


11 


5. 


91 


4. 


96 


19.7? 


1990 


46 


7. 


11 


5. 


91 


4. 


96 


19.73 


Syria 


















1984 


508 


326. 


11 


264. 


16 


75. 


27 


210.51 


1985 


554 


380. 


97 


302. 


42 


76, 


82 


214.16 


1986 


539 


385. 


76 


305. 


47 


71. 


75 


198.81 


1987 


541 


418. 


42 


320. 


09 


70. 


66 


195.83 


1988 


528 


439. 


14 


326. 


21 


68. 


55 


190.29 


1989 


498 


434. 


23 


310. 


59 


69. 


40 


192.93 


1990 


480 


544. 


74 


347. 


32 


65. 


60 


181.34 


Tunisia 


















1984 


8 


• 


00 


• 


00 




00 


5.65 


1985 


20 


• 


00 


, 


00 


5! 


20 


21.06 


1986 


22 


• 


00 


• 


00 


6. 


07 


23.56 


1987 


22 


• 


00 


• 


00 


6. 


07 


23.56 


1988 


22 


• 


00 


• 


00 


6. 


07 


23.56 


1989 


22 


• 


00 


■ 


00 


6. 


07 


23.56 


1990 


22 


• 


00 


■ 


00 


6. 


07 


23.56 


United Arab 


Emirates 
















1984 


40 


6. 


25 


4. 


80 


• 


42 


5.89 


1985 


39 


6. 


00 


4. 


61 


. 


42 


5.89 


1986 


53 


8. 


92 


6. 


29 


1. 


62 


10.31 


1987 


59 


17. 


61 


10. 


71 


2. 


42 


13.17 


1988 


67 


26. 


30 


15. 


14 


3. 


21 


16.03 


1989 


75 


34. 


10 


19. 


63 


3. 


21 


16.03 


1990 


75 


34. 


10 


19. 


63 


3. 


21 


16.03 



201 - 



YEAR 



North Yemen 

1984 
1985 
1986 
1987 
1988 
1989 
1990 

South Yemen 

1984 
1985 
1986 
1987 
1988 
1989 
1990 



INVENTORY AIR 

DEFENSE 



75 
73 
73 
73 
73 
73 
73 



104 
104 
104 
104 
104 
98 
110 



3.53 
3. 39 
3. 39 
39 
39 
39 
39 



13.41 
13.41 
13.41 
12. 38 
19. 38 
25. 78 
35.03 



2 


.82 


2, 


. 71 


2. 


, 71 


2. 


, 71 


2. 


71 


2. 


71 


2. 


71 


10. 


91 


10. 


91 


10. 


91 


9. 


93 


13. 


01 


15. 


58 


20. 


65 



INTERDICTION 



2. 74 
2. 66 



2. 66 
2. 66 
2. 66 
2. 66 
2. 66 



6. 03 
6. .03 
6. 03 
6. 03 
5. 67 
5. 67 
5. 67 



CAS 



8. 33 
8. 05 
8. 05 
8. 05 
8. 05 
8. 05 
8. 05 



16 
16 
16 
16 

16. 27 
16. 27 
16. 27 



64 
64 
64 
64 



•202 



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